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Control, Decision and Information Technologies (CoDIT), 2013 International Conference on

Date 6-8 May 2013

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Displaying Results 1 - 25 of 171
  • [Front cover]

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  • Consensus based approaches for distributed estimation, optimization and control

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  • Table of contents

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  • Discretization of singular systems and error estimation

    Page(s): 001 - 006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (660 KB) |  | HTML iconHTML  

    This paper proposes a discretization technique of a descriptor differential system. The methodology used is both triangular first order hold (TFOH) discretization and zero order hold (ZOH) for the input function. Upper bounds for the error between the continuous time solution and the discrete time solution are produced for both discretization methods and are shown to be better than other approximations in the literature. View full abstract»

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  • Model Predictive Control based on the ARX-Laguerre model

    Page(s): 007 - 012
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (674 KB) |  | HTML iconHTML  

    In this paper, we propose to use the Model Predictive Control (MPC) based on the ARX (Auto Regressive with Exogenous Input) model expansion on Laguerre orthonormal bases, which provides a better performances with respect to the classical ARX model. The resulting model is entitled the ARX-Laguerre model that enables to identify a large class of linear systems with drastically reduction of involved parameters. An ℓ2 - norm finite moving horizon cost function is used to obtain the control law formulated as an analytical expression. View full abstract»

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  • Nonlinear system identification using new Extended Possibilistic C-Means Algorithm and Particle Swarm Optimization

    Page(s): 013 - 020
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (827 KB) |  | HTML iconHTML  

    The development of a mathematical model making it possible to represent as well as possible the dynamic behaviors of a complex real process represents a very important problem in the real world. Fuzzy logic and more, particularly the Takagi-Sugeno (TS) fuzzy model draws the attention of several researchers during these last decades. This is due to their capability to approximate the nonlinear system in several locally linear subsystems. Many clustering algorithms exist in literature allowing the identification of the parameters intervening in the TS fuzzy model. In this paper a new clustering algorithm noted NEPCM-PSO is proposed. The proposed algorithm represents a combination between New Extended Possibilistic C-Means algorithm (NEPCM) and Particle Swarm Optimization (PSO) algorithm. The effectiveness of this algorithm is tested on a nonlinear system and on an electro-hydraulic system. In this paper a comparative study between PCM algorithm, NEPCM algorithm and NEPCM-PSO algorithm are also presented. View full abstract»

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  • A reduced order observer for Switching-Mode Model state estimation

    Page(s): 021 - 026
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (815 KB) |  | HTML iconHTML  

    In this paper, we investigate the application of a reduced-order observer for state estimation of freeway traffic flow described by the so-called Switching Mode Model (SMM). SMM is derived from the Cell Transmission Model and then applied to the reconstruction of the traffic density, considered as state variable. As the SMM is a hybrid system which switches depending on the traffic conditions along the freeway, the paper shows how, the proposed estimation method takes into account such different modes. Several numerical simulation with different data demonstrate the relevance of the proposed algorithm. View full abstract»

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  • Simplification and linearization of a greenhouse model directly on bond graph

    Page(s): 027 - 031
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (897 KB) |  | HTML iconHTML  

    The greenhouse is a complex, non-linear and high order system, it combines two fields “hydraulic and thermal”, and then it is not easy to analyze and control it. Hence, simplification is used in order to obtain model which facilitate the global analysis. In this paper we present a linear and simplifed bond graph model of a greenhouse from the model developed by Abbes [1]. The MORA method is used for simplification, generally used for the reduction of monoenergetic system, in this paper we enlarged this method for the reduction of the multienergetic system. This linear model will be used later to analysis and control a greenhouse system. View full abstract»

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  • Modified support vector machines for MR brain images recognition

    Page(s): 032 - 035
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (678 KB) |  | HTML iconHTML  

    Support vector machine (SVM) is a popular method of learning classification with lots of applications. In this work, we extend SVM to recognize the appearance of tumors in MR brain image. Parameterization of the kernel in SVM learning procedure, along selecting features, influences the accuracy of the recognition and increases the computational effect. For this, a Shuffled Frog Leaping Algorithm (SFLA) based approach for feature selection of the SVM, termed SFLA-SVM, is developed. To demonstrate the quality of our technique, we give some experiments on MR brain images. View full abstract»

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  • Real time implementation of medical images segmentation based on PSO

    Page(s): 036 - 042
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1057 KB) |  | HTML iconHTML  

    Segmentation is an important technique that is applied to prepare image for detection, classification and recognition steps. To successfully achieve these steps, we have to successfully realize the segmentation step. Therefore, several approaches have been developed yet. One of these algorithms is based on the technique of Particle Swarm Optimization (PSO). It has been widely applied in the literature. This paper deals with hardware implementation of PSO algorithm for medical images segmentation using Xilinx System Generator (XSG). The use of the visual development process models of Simulink facilitates the RTL level simulation and validation such as the synthesis of VHDL code. Also, all legacy codes written in Matlab can be used again in custom blocks. The performances of the proposed method are demonstrated using a set of medical images. View full abstract»

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  • Segmentation of radar images by combining watershed and Fisher techniques for target classification

    Page(s): 043 - 046
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (782 KB) |  | HTML iconHTML  

    In the area of monitoring and observation by sea, land or air by the use of information is an important task for the decision. We are interested in the present work on the information processing for the recognition of radar targets from ISAR images. To provide the information necessary for the recognition phase, we propose an approach radar image processing based on the combination algorithms Fisher and watershed. This combination allows us to obtain closed shapes (edge). Then, we proceed to a shape modeling based on the Fourier descriptor. Finally, we use the KNN classifier to evaluate the performance of our approach. The obtained results seem good. View full abstract»

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  • Bagging support vector machine approaches for pulmonary nodule detection

    Page(s): 047 - 050
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (728 KB) |  | HTML iconHTML  

    In this paper, pulmonary nodules extracted from computed tomography (CT) images are classified by the single and bagging support vector machine (SVM) classifiers. To determine features, two dimensional principal component analysis is performed. In order to select the best features, three different models are proposed. These models are tested with classifiers of both single SVM and bagging SVM. As a result of tests, bagging SVM is shown to be superior to single SVM. View full abstract»

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  • Observer-based fault tolerant tracking control for vehicule lateral dynamics

    Page(s): 051 - 056
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (739 KB) |  | HTML iconHTML  

    This paper deals with actuator fault tolerant tracking controller design for a vehicle lateral dynamics represented by the uncertain Takagi-Sugeno (T-S) fuzzy model. The main idea is based on the use of a Proportional Integer Observer (PIO) to estimate both constant faults and faulty system states combined to the descriptor redundancy property. The objective of this approach is to synthesize a Fault Tolerant Controller (FTC) ensuring trajectory tracking of a desired reference vehicle model. To take account variation of tire slip angles, the case of unmeasurable premise variables is considered and a solution is then proposed in terms of Linear Matrix Inequalities (LMI). Simulation results demonstrate the effectiveness of the proposed method. View full abstract»

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  • Comparative study of PCA approaches for fault detection: Application to a chemical reactor

    Page(s): 057 - 062
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1005 KB) |  | HTML iconHTML  

    Principal component analysis (PCA) has been successfully applied to several monitoring problems. However, classical PCA which is a fixed-model approach has some limitations: one of these is the inability to deal with parameter-varying process. Then an adaptation mechanism is recommended. This paper suggests a recursive PCA method for fault detection based on First Order Perturbation (RPCA-FOP). It also compares the effectiveness of the presented RPCA-FOP method and two other PCA techniques existing in literature such as the conventional PCA and the sliding window principal component analysis (SWPCA). The considered performances which are the average computation time, the missed detection rate and the false alarm rate are evaluated by simulation on a Continuous Stirred Tank Reactor (CSTR). View full abstract»

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  • Robust sensor fault reconstruction of uncertain time-delay systems using an extended Sliding Mode Observer

    Page(s): 063 - 070
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    In this paper, we propose a robust sensor fault reconstruction method for a class of linear uncertain delayed systems using an extended Sliding Mode Observer (SMO). The uncertainty considered is matched and bounded. In meantime, the time-varying delay is unknown, bounded and affects simultaneously the input and the state of the system. Using the H concept and applying the equivalent output error injection idea from previous works in Fault Detection and Isolation (FDI) scheme, the estimate sensor fault signal is designed with the proposed observer that minimizes the uncertainty and the time-varying delay effects. Therefore, this problem is solved via Linear Matrix Inequalities (LMIs) optimization. A numerical example is given to illustrate the validity and the applicability of the proposed approach. View full abstract»

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  • Active fault diagnosis for immersed structure

    Page(s): 071 - 075
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (838 KB) |  | HTML iconHTML  

    The immersed system environment makes delicate their diagnosis. Such systems encountered in energy production sites (stream turbines), water treatment or oil platform are not easily accessible for diagnosis issues. Most of dedicated diagnosis techniques are expensive, need a precise positioning of sensors and present large delay. The need to develop alternative techniques is therefore justified. The proposed contribution is a part of this approach. It is based on active fault diagnosis and detection. The measurements are performed by ultrasonic echography. The proposed method combines signal-processing tools to characterize the measurements and artificial neural networks (ANNs) for classification and decision. View full abstract»

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  • UIO based robust fault detection and estimation

    Page(s): 076 - 081
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (602 KB) |  | HTML iconHTML  

    This paper deals with the robust actuator fault diagnosis by using an Unknown Input Observer (UIO). The proposed UIO design guarantees robust residual generation through decoupling the disturbances effects from the fault ones. A Generalized Observer Scheme (GOS) based on a bank of unknown input observers is exploited for fault isolation. The fault estimation is, then, allowed by an algebraic transformation. A simulation case study is given to support the theoretical development. View full abstract»

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  • Expansion of an hypoelliptic heat-kernel outside the cut-locus in semi-group theory

    Page(s): 082 - 085
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB) |  | HTML iconHTML  

    We give a proof in semi-group theory based on the Malliavin Calculus of Bismut type in semi-group theory and Wentzel-Freidlin estimates in semi-group of our result giving an expansion of an hypoelliptic heat-kernel outside the cut-locus. View full abstract»

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  • PID-PSO control for Takagi-Sugeno Fuzzy model

    Page(s): 086 - 091
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (718 KB) |  | HTML iconHTML  

    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. View full abstract»

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