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International audience; In this work, robustness and convergence properties of model reduction are investigated for discrete two-dimensional (2D) systems in the Fornasini-Marchesini (F-M) model with polytopic uncertainties. The goal is to design a reduced order model minimizing H ∞ performance in a known finite-frequency (FF) area of the noises able to reproduce the behavior of the uncertain 2D original system. Using Lyapunov function and generalized Kalman Yakubovich Popov (gKYP) lemma, sufficient conditions for the existence of the FF reduced order design approach are formulated as feasibility of a set of Linear Matrix Inequalities (LMIs). Numerical simulations are given to illustrate the validity and feasibility of the designed reduced-order model.

International audience; he problem of active Fault Tolerant Control (FTC) for a Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems with time delays subject to an actuator fault, is investigated in this paper. This new approach consists on constructing an adaptive Unknown Input Observer (UIO) using linear parameter time varying model firstly and to design a control law secondly. Based on linear matrix inequality (LMI) techniques, the FTC linear parameter-varying (LPV) control scheme is developed for the LPV system. The stability of the proposed FTC via closed-loop system is verified by Lyapunov approach. Finally, the effectiveness of the proposed method is illustrated via simulation results.

The focus of this paper is the analysis and the design of H∞) observer-based fault-tolerant controller (FTC) for the time-delay system. The FTC is generated based on states, actuator and sensor faults reconstruction given by the observer. For the stability issue, a Lyapunov Krasovskii functional is used and obtained in the form of Linear Matrix Inequality (LMI). Finally, the feasibility and efficiency of the theoretical results presented is provided by a numerical example.

Due to stronger environmental standard aims, the European Union (EU) has recently adopted more stringent limits for emissions from inland waterway vessels. The objective of “RIVER” project is to apply an oxyfuel combustion technology for diesel engines that eliminates NOx emissions, and captures and stores all carbon dioxide emissions in order to achieve zero-carbon and zero other pollutant emissions. As part of this project, a 3-D computational fluid dynamics model coupled with detailed chemical kinetics has been used to evaluate the influence of oxyfuel combustion on engine operating conditions and combustion characteristic in a high speed direct injection (HSDI) diesel engine under homogenous charge compression ignition (HCCI) mode. In this work, a reduced chemical n-heptane-n-butanol-PAH mechanism which consists 76 species and 349 reactions has been applied to simulate the combustion process. The mechanism has been initially validated by experiments under HCCI combustion mode and then, it has been used to examine the oxyfuel combustion using different diluent strategies over a range of air-fuel equivalence ratio (lambda). The simulation results indicate that increasing the inlet carbon dioxide concentration, as a diluent gas, under constant fueling rate does not bring any serious change to the amount of brake mean effective pressure (BMEP) in the relatively rich mixtures regions. However, by decreasing the fuel rate (higher lambda) the difference between different diluent strategies become more obvious as the minimum amount of BMEP is achieved when 83% of carbon dioxide is used. In addition, the results show a considerable reduction of PM emissions while the NOx emission have been completely eliminated using oxyfuel combustion.

In this paper, the imitation of central nervous system control of human lower limb is done and the controller simulation for ankle joints is presented. The CNS control is applied to multi-joints coordination relating to the problem of human postural balance. This control strategy was developed with aid of state-space model of the lower extremity of human in order to reconstruct natural human motion and to stabilize the human posture. Based on real model experiments, which simulates the human motion process in real time, we present the methodology of actual target values obtaining which correspond to position of human upright standing.

This paper presents an improved model of nonlinear actuator, as example of three-phase asynchronous motor, being an integral part of automatic mooring winch. For such technical objects, a mathematical model of auxiliary parametric sensitivity was developed, which becomes the real unifying factor that make it possible to analyze the nonlinear technical systems based on general theory of nonlinear differential equations combining into one algorithms all stages of analysis: calculation of transitive and steady-state processes, determination of static stability and parametric sensitivity.

The analysis is comparing the scientific journals of the field of IC and knowledge management. The results are presented under tables and graphs and, are commented to make a comparison between these journals. The overall objective of the final work would be the implementation of scientometric indexes using the programming language R and the interpretation of the obtained results. The originality of the paper is the proposal of relative indexes to a query, i.e., relatively to a set of papers and not applied to the overall research database.

In this paper, we reviewed business intelligence in scientific research aspect, its existing tools on the market; its situation in the world and its situation in Algeria. We then analyzed the situation of the business intelligence at SCIMAT company which is our case study. We proposed a moderated monitoring system in management of alerts and monitoring of web pages, management of external events or internal to the production systems (training, conferences…etc.) and its most consistent module; a configurable and customizable survey generator (which we have called STL 1.0) into categories of questions, types of questions, the choice of languages to allow the members of the sample, to better understand the questions and to answer them correctly, come later the statistical calculations which are on several parts; as a positive response rate tables questions, accompanied by charts.We have applied this tool to generate several e-questionnaires or e-surveys that make it possible to make global or regional cartographies on the studied aspects.

This paper deals with a fault sensor detection and estimation based on Unknown Input Observer (UIO) for vehicle lateral dynamics. The vehicle lateral dynamics is represented by a fourth degree of freedom model. This nonlinear model is transformed into linear parameter varying model where the longitudinal velocity is considered as parameter varying. Then, an Unknown Input Observer is designed in order to reconstruct the state variables in presence of sensor faults.Based on Lyapunov theory, the observer gains are computed by Linear Matrix Inequalities. The approach can discriminate sensor faults from disturbances. Simulation results are given to show the effectiveness of the proposed approach to detect sensor faults subject to disturbances.

This paper proposes a new T-S fuzzy method to deal with the power tracking problem of the power generating systems. First, a dynamic model of the PV system is developed which is subsequently converted into a Takagi-Sugeno (TS) model. Then, after leading to this exact model, a fuzzy controller is designed to obtain the MPP even during the variation of the climatic conditions. Based on the T–S fuzzy model, the fuzzy maximum power point tracking controller is designed by constructing fuzzy gain state feedback controller and an optimal reference model for the optimal PV output voltage, which corresponds actually to maximum power point (MPP).The controller gains are obtained by solving a set of linear matrix inequalities (LMIs). Finally, simulation results are given to illustrate the optimal tracking performance of the proposed fuzzy controller even in the case of abrupt changing climatic conditions.

This paper focuses on the methodology of estimating dynamic states of the vehicle, by a sliding-mode observer (SMO). The vehicle is represented by the fuzzy model of Takagi Sugeno (TS). The main contribution of this article is the development of a robust observer with the synthesis of sufficient stability conditions of this observer. This representation is used to take into account the non-linearities of the lateral forces. The stability of the fuzzy observer is given by the Lyapunov approach; the stability conditions of this observer are expressed in terms of linear matrix inequalities (LMI). The simulation results show the effectiveness of the proposed observer in estimating the states of the vehicle dynamics.

This paper studies the sideslip angle estimation problem of a ground vehicle in a finite frequency (FF) domain. The main objective is to estimate the sideslip angle using the yaw rate measurements. Filter design conditions are developed based on the uncertain lateral dynamics model, and time domain interpretations of the kalman Yakubovich Popov lemma (GKYP lemma). The designed filter ensures that the estimation error system is stable and has a prescribed H attenuation level, over a specified FF domain of the vehicle steering angle. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

International audience; This paper proposes an actuator fault detection based on unknown input observer for simultaneously state estimation and fault estimation applied to lateral vehicle dynamics. The lateral dynamics is modeled by a linear parameter varying model where the longitudinal velocity is considered as parameter varying. The observers gains are based on the resolution of linear matrix inequalities. The observers convergence is analyzed by using the Lyapunov theory. The proposed fault detection system is finally applied to the detection and estimation of steering angle fault and the estimation of the yaw velocity, yaw angle and the trajectory of the vehicle. Simulation study verifies the effectiveness of the proposed approach.

As the photovoltaic produced power is depending on weather conditions, Maximum Power Point Tracking (MPPT) controller are usually applied to extract the maximum amount of generated power. This work provides a review of MPPT controllers based on fuzzy logic concept. The studied system includes a PV panel, a DC-DC converter and a resistive load. The classical MPPT algorithms fail to track the maximum peak power point (MPP) accurately, so that fuzzy logic is introduced to reduce oscillations around the operating point as it can trace the maximum power for all the cases. Three fuzzy Logic Controllers (FLC) are developed using different types, Mamdani and Takagi-Sugeno (T-S), and different input and output signals. The proposed algorithms are tested under dissimilar atmospheric conditions and compared to a conventional MPPT technique, Perturb and Observe. FLCs provide quick reaction to the weather conditions variation and decreased fluctuations around the MPP.

This paper presents a robust observer-based $H_\infty$ controller design method via LMIs for a class of switched discrete-time linear systems with $l_2$-bounded disturbances and parameter uncertainties. The main contribution of this paper consists in a new and judicious use of the slack variables coming from Finsler's lemma. We show analytically how the proposed slack variables allow to eliminate some bilinear matrix coupling. The validity and effectiveness of the proposed design methodology are shown through two numerical examples.

This paper deals with Takagi-Sugeno fuzzy logic applied to an autonomous photovoltaic system for power maximization. It includes a DC-DC buck converter state model connected between the PV panel and the load in order to maximize the output power. The TS fuzzy MPPT controller is compared to the classical P&O and fuzzy Mandani algorithms. Results show that: thanks to its capacity to transform the non-linear system in a set of local linear sub-systems, Takagi-Sugeno approach leads to better performances.

This paper presents the analysis, design and simulation results of a dc-dc LLC resonant converter fed by photovoltaic (PV) panels. In order to achieve MPPT (Maximum Power Point Tracking), a Variable Inductor is used to control the converter operation. In order to analyze the converter operation a simple dc load (resistor with fixed value) is considered and simulations results are presented considering different irradiance conditions of the PV panels. The system specifications are presented and simulation results were obtained considering different irradiance conditions.

This work aims at designing a Maximum Power Point Tracking (MPPT) controller to optimize the photovoltaic (PV) produced power. For each pair of temperature and solar irradiance, there is a single operating point corresponding to the maximum power delivered to the load. As being a nonlinear system, the photovoltaic (PV) system can be described by Takagi Sugeno (T-S) fuzzy model. The proposed MPPT control scheme which allows the maximum power transferring from the PV panel to the load is mainly based on Parallel Distributed Compensation (PDC) technique. The control gains have been computed based on Linear Matrix Inequalities tools (LMI). Lyapunov approach has been applied to prove the stability of the system. The performance of the proposed controller is exhibited by simulation results.

This paper deals with the design of a fault detection and isolation (FDI) of the sensors for an induction machine using nonlinear analytical redundancy (NLAR). This paper investigates the detection and isolation of faults using elimination of unknown variables of the system and in particularly the unknown system states. The induction machine (IM), it is highly nonlinear, multivariable, time-varying system and particularly when subject to the faults, it is difficult to detected them by linear approaches. The nonlinear parity space algorithm is able to detect and isolate sensor faults such IM speed and stator currents or actuator faults (stator voltage). In order to prove the accuracy of approaches studying in this work, simulation results will be given.

International audience; This note deals with the problem of observer-based stabilization for discrete-time linear systems with norm-bounded parameter uncertainties. Thanks to slack variable technique and the two-step method, an LMI-based approach is provided to compute simultaneously all the main decision variable of the observer-based controller problem. Our approach is inspired from the classical two-steps method introduced by Stankovic et al. and the modified two-steps method introduced by Zemouche et al.. Some comments are reserved to emphasize and clarify the difference between the different variants of the two-steps method.A numerical example is provided in order to illustrate how the new algorithm is less conservative than previous results in literature.

This paper is devoted to the study of observer-based ℋ ∞ controller design method via LMIs for a class of switched discrete-time linear systems with l 2 -bounded disturbances. The main contribution of this work consists in a new and judicious use of the slack variables coming from Finsler's lemma. We clarify analytically how the proposed slack variables allows to eliminate some bilinear matrix coupling. The validity and effectiveness of the proposed design methodology are illustrated through a numerical example.