Browsing by Author "Zelmat, M."

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Algerian energy policy and potential to reducing greenhouse gas emissions
(Taylor & Francis, 2016) Sahnoune, Fatiha; Belhamel, M.; Zelmat, M.
In Algeria, energy consumption is growing rapidly. Due to the high demand and declining conventional energy resources, the country developed a new energy policy based on the promotion of renewable energies and CO2 mitigation. For this purpose, the authorities have taken new steps to reinforce the legislative and institutional framework and put in place a favorable financial support. In addition, an ambitious program to develop solar energy and energy efficiency was adopted recently. The objective of this strategy is to achieve by 2030 a 40% share of solar electricity and to contribute, voluntarily, to reducing greenhouse gas (GHG) emissions. In this work, we present an analysis of the current situation and its projection to 2030 and we examine the available opportunities to reduce significantly GHG emissions. The study presents the magnitude of emissions in 2008 and 2012, the temporal evolution of CO2 and particularly what will be the impact on GHG emissions of the new strategy. The results show that, in 2012, GHG emissions totaled 153 MT CO2 eq. and growing at a rate of over 3%. However, there is a high potential for mitigation, especially in energy sectors, in building, transportation, as well as waste management and gas flaring. Two scenarios are developed: with implementation of solar energy and without. This analysis shows that the overall potential of CO2 mitigation will rise in 2030 to about 300 MT CO2 eq
Bearing damage detection and diagnosis by multi-scale PCA and Power quality distribution of rotating machinery
(2011) Baiche, Karim; Zelmat, M.; Lachouri, A.
Chaos synchronization based on unknown input proportional multiple-integral fuzzy observer
(Hindawi, 2013) Youssef, T.; Chadli, M.; Karimi, R. M.; Zelmat, M.
This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method
A combined IQG/LTR and fuzzy logic strategy for the improved control of a drum-type boiler-turbine plant
(2009) Habbi, H.; Kidouche, M.; Zelmat, M.
This paper addresses the design of a fuzzy control system with a fuzzy controller and a fuzzy estimator for a fossil-fuelled drum-type boiler-turbine unit. The fuzzy control method is based on a dynamic Takagi-Sugeno (TS) fuzzy model which has been developed in Habbi et al. [Automatica 39 (2003) 1213] for the nonlinear steam power plant. In the design procedure, a dynamics augmentation is first suggested and a dynamic fuzzy augmented system is determined to deal with the non-minimum phase behaviour of the plant. The global fuzzy control system is designed from a local concept viewpoint using the optimal control theory. To assess the performance of the proposed optimal fuzzy controller, simulations under various operation conditions including actuators saturation are performed over a wide operating range of the physical plant
Comparative study between solar and conventional heating-economic study and environmental impact
(Elsevier, 2014) Sahnoune, Fatiha; Madani, M.; Zelmat, M.; Belhamel, M.
Design of unknown inputs proportional integral observers for TS fuzzy models
(Elsevier, 2014) Youssef, T.; Chadli, M.; Karimi, H.R.; Zelmat, M.
A dynamic fuzzy model for a drum–boiler–turbine system
(2003) Habbi, H.; Zelmat, M.; Ould Bouamama, B.
A nonlinear dynamic fuzzy model for natural circulation drum–boiler–turbine is presented. The model is derived from Åström–Bell nonlinear dynamic system and describes the complicated dynamics of the physical plant. It is shown that the dynamic fuzzy model gives in some appropriate sense accurate global nonlinear prediction and at the same time that its local models are close approximations to the local linearizations of the nonlinear dynamic system. This closeness is illustrated by simulation in various conditions
The first integral approach in stability problem of large scale nonlinear dynamical systems
(2008) Kidouche, M.; Habbi, H.; Zelmat, M.; Grouni, S.
In analyzing large scale nonlinear dynamical systems, it is often desirable to treat the overall system as a collection of interconnected subsystems. Solutions properties of the large scale system are then deduced from the solution properties of the individual subsystems and the nature of the interconnections. In this paper a new approach is proposed for the stability analysis of large scale systems, which is based upon the concept of vector Lyapunov functions and the decomposition methods. The present results make use of graph theoretic decomposition techniques in which the overall system is partitioned into a hierarchy of strongly connected components. We show then, that under very reasonable assumptions, the overall system is stable once the strongly connected subsystems are stables. Finally an example is given to illustrate the constructive methodology proposed
Fuzzy logic based gradient descent method with application to a PI-type fuzzy controller tuning : new results
(IEEE, 2007) Habbi, A.; Zelmat, M.
Implementation of a fuzzy logic system to tune a PI controller applied to an induction motor
(2009) Laroussi, K.; Zelmat, M.; Rouff, M.
The simplicity of traditional regulators makes them popular and the most used solution in the nowadays industry. However, they suffer from some limitations and cannot deal with nonlinear dynamics and system parameters variation. In the literature, several strategies of adaptation are developed to alleviate these limitations. In this paper, we propose a combination of two strategies for PI parameters supervision and adaptation. We apply the obtained structure to the control of induction machine speed. Simulation and experimental results of the proposed schema show good performances as compared to two strategies
Modelling the nonlinear dynamic behaviour of a boiler-turbine system using a radial basis function neural network
(John Wiley and Sons, 2014) Kouadri, A.; Namoun, A.; Zelmat, M.
Pedestrian tracking using color, thermal and location cue measurements : a DSmT-based framework
(Springer, 2012) Airouche, Mohamed; Bentabet, L.; Zelmat, M.; Gao, G.
A perfectly symmetrical configuration in dual-bridge inverter topology for maximum mitigation of emi, common-mode voltages and common-mode currents
(2010) Akroum, Hamza; Kidouche, M.; Grouni, S.; Zelmat, M.
Performance improvement of large-scale solar water heating systems by using remote monitoring
(Revista Tecnica de la Facultad de Ingeniera, 2015) Sahnoune, F.S.; Belhamel, M.; Zelmat, M.
Prediction of boiler output variables through the PLS linear regression technique
(2011) Kouadri, A.; Zelmat, M.; Albarbar, A.
A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank
(Taylor & Francis, 2010) Kouadri, A.; Chiter, L.; Zelmat, M.
In many physical systems, it is difficult to obtain a model structure that is highly nonlinear and complex. However, models are usually linear, but not suitable in such form to model processes because they contain a significant number of simplifying hypotheses which are insufficient for the design of reliable controllers. The absence of robustness with respect to system parameters does not ensure the performance specifications of the control system knowing that the nominal parametric state rarely corresponds to the real one. For these raisons, it is beneficial to use a specific technique to characterize accurately system dynamics in an entirely uncertain environment. In this work, we present an approach to approximate and validate over a large operating range the dynamic behaviour of a Three Tank System benchmark based on a radial basis function neural network (RBFNN). The proposed RBFNN is applied to solve the parametric-identification problems for nonlinear and complex system by using a modified DIRECT algorithm to search the network parameters. The learning algorithm is developed by combining the DIRECT algorithm and a linear regression for fast convergence. Different experimental results have been performed to show the effectiveness of the RBFNN model to emulate the dynamic behaviour of the nonlinear and complex system under different situations
Stability of interconnected systems under structural perturbation : decomposition- aggregation approach
(2008) Kidouche, M.; Habbi, H.; Zelmat, M.
In this paper, the decomposition-aggregation method is used to carry out connective stability criteria for general linear composite system via aggregation. The large scale system is decomposed into a number of subsystems. By associating directed graphs with dynamic systems in an essential way, we define the relation between system structure and stability in the sense of Lyapunov. The stability criteria is then associated with the stability and system matrices of subsystems as well as those interconnected terms among subsystems using the concepts of vector differential inequalities and vector Lyapunov functions. Then, we show that the stability of each subsystem and stability of the aggregate model imply connective stability of the overall system. An example is reported, showing the efficiency of the proposed technique
A statistical-based approach for fault detection in a three tank system
(Taylor & Francis, 2013) Kouadri, A.; Namoun, A.; Zelmat, M.; Aitouche, Moh-Amokrane
Fault detection in stochastic dynamical systems is usually carried out by the generation of residuals directly reflecting the magnitude of the faults. For this purpose, faults indicator is used to evaluate possible deviations from the normal operating conditions and the measurements of the system. This evaluation is often very difficult to implement in the multi-faults case. This article aims to demonstrate the efficiency of the coefficient of variation (CV) in detecting single and multi-faults in a multivariable laboratory three tank system DTS-200. The performance of the detection algorithm is based on the computation of the confidence intervals (CIs) which provide an estimate of the amount of error in the considered data and characterise the precision of the computed statistical estimates. The data variability may result from random measurement errors caused by the system parameters uncertainties, internal and external noises, and measuring instrument, which are not usually accurate. The CIs make the CV less sensitive to parameter uncertainties and to measure noises. The robustness and accuracy of the CV are shown in a healthy mode and various faulty situations in an entirely uncertain environment
Une approche améliorée pour l'optimisation des contrôleurs flous
(Journal Europeen des Systemes Automatises, 2002) Habbi, H.; Zelmat, M.
In this paper, an improved adaptation mechanism for tuning of fuzzy logic controllers using gradient descent method is proposed. The proposed algorithm is used for input/output membership functions tuning of a fuzzy controller by minimising some criterion on the control output. The optimisation problem is solved using gradient descent technique. In this tuning procedure, the constant which controls how much the fuzzy controller parameters are altered at each iteration is updated using a fuzzy logic approximate reasoning modelled as a set of IF-THEN rules. To illustrate the usefulness and the effectiveness of the improved algorithm, we consider the problem of minimising the matching error induced by an additive noise affecting the input information of a Pi-like fuzzy controller
Unknown input proportional integral observer design for chaotic TS fuzzy models
(Springer, 2013) Youssef, T.; Chadli, Mohammed; Zelinka, Ivan; Zelmat, M.