Doctorat
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/59
Browse
149 results
Search Results
Item Adaptive control of drone by rejection of disturbances(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Hadid, Samira; Boushaki, Razika(Directeur de thèse)Quadrotor or Unmanned Aerial Vehicles (UAVs), a popular type, use four propellers for flight and are gaining popularity due to their versatility and ease of use. Interest in controlling UAVs has significantly increased recently. This work focuses on the control and trajectory planning challenges of quadrotors. While many studies address disturbances and faults, the inherent underactuation (four inputs controlling six degrees of freedom) makes precise control and trajectory tracking difficult, particularly in complex scenarios. The research aims to improve quadrotor control in challenging environments. The Newton-Euler method is used in this work to develop the quadrotor's dynamic model. Then, an exploration using Dyna-Q reinforcement learning for autonomous quadrotor navigation in complex environments. The algorithm allows the quadrotor to learn optimal flight paths through trial and error. In addition, this thesis presents an in-depth investigation into improving the autonomy and control capabilities of quadrotors. The focus is on developing and implementing various linear and nonlinear control strategies to regulate the behavior of quadrotor UAVs. Each control strategy is carefully adjusted and fine-tuned to achieve the desired dynamic response and stability during quadrotor flight. Following that, we provide a comparison of the designed controllers. It then focuses on comparing the performance of fractional-order PID (FOPID) and sliding mode control (SMC) for trajectory tracking, emphasizing robustness against disturbances and nonlinearities. Furthermore, the research introduces an intelligent trajectory planning system using Dyna-Q learning to enable autonomous navigation and obstacle avoidance in complex environments, enhancing quadrotor adaptability and responsiveness for various applications. Extensive simulations validate the proposed control strategies and trajectory planning. Overall, this study contributes significantly to the field of quadrotor control and autonomy, providing valuable insights and solutions for improving flight stability and enabling secure and efficient operations in a variety of real-world scenarios.Item SoC estimation for optimal ESS’ energy management(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Zermout, Abdelaziz; Belaidi, Hadjira(Directeur de thèse)Battery energy storage systems have become indispensable to modern civilization, enabling the functionality of numerous advanced technologies, including high-performance smartphones, long-range electric vehicles, and various portable electronic, tools, and backup systems. The continuous advancement of battery technology is a key driver for future innovations. A crucial component of battery systems is the Battery Management System (BMS), which monitors and optimizes various operational parameters, including the State of Charge (SoC). SoC represents the remaining useful battery capacity relative to its total capacity, however it cannot be directly measured and must be estimated through computational techniques instead. While existing estimation methods have significantly improved in terms of accuracy and reliability, they remain challenged by complexity, sensitivity to operating conditions, and dependence on dynamic load behavior. Overcoming these challenges is essential for enhancing the performance and longevity of battery systems in next-generation applications. Our contribution is a novel estimation technique that periodically stimulates the battery with a predefined current profile during charging or discharging to determine its State of Charge (SoC). Since this method is not continuous, it is combined with Coulomb counting for calibration. The results demonstrated the method's efficiency and reliability, effectively overcoming dependency on environmental conditions and dynamic load behavior. Its key advantages include independence from operating conditions and dynamic load behavior, as well as, minimal computational complexity without sacrificing accuracy achieving an error of less than 1%. This ensures high reliability and efficiency with reduced complexityItem Multivariate statistical process monitoring using kernel statistical techniques(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Kaib, Mohammed Tahar Habib; Harkat, Mohamed Faouzi(Directeur de thèse)Fault Detection and Diagnosis (FDD) is an important part of industrial plants because monitoring systems are responsible for capturing faults as soon as they occur to avoid major casualties in equipment, operators, and the environment......Item Etude et conception d'une antenne patch à bande rejetée(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Fortas, Ibrahim; Ayad, Mouloud(Directeur de thèse)Les communications sans fil connaissent une expansion rapide, nécessitant des solutions innovantes face à la hausse des besoins en débits élevés et à la saturation du spectre. La technologie ultra-large bande (ULB) se présente comme une alternative prometteuse. Dans le cadre de cette thèse, deux nouvelles antennes ULB ont été proposées : une antenne élémentaire patch à doubles ellipses alimentée par une ligne coplanaire (CPW) et une antenne microruban réseau de dipôles logarithmiquement périodique (MLPDA) alimenté par deux lignes microrubans. Etant donné que les systèmes ULB peuvent générer des interférences avec les applications existantes (WiMAX, WLAN, bande X, etc.), l'intégration de mécanismes de bandes rejetées s'avère essentielle. Pour y remédier, des cellules à métamatériaux sous forme de résonateurs en boucle ouverte ont été placées près du patch de la première antenne, tandis que des stubs rectangulaires ont été connectés à la ligne microruban de la seconde antenne. Ces techniques permettent de rejeter les bandes WLAN et Satellite DL (bande X) pour l’antenne patch, ainsi que les bandes WiMAX et WLAN pour l’antenne MLPDA. Les performances des antennes ont été analysées via des simulations sous CST Studio, puis validées par des mesures expérimentales. Les résultats obtenus montrent une bonne concordance entre simulations et expérimentations, confirmant l’efficacité des solutions proposées pour répondre aux exigences des communications sans fil modernes en termes de large bande et de rejet d’interférencesItem Interval-valued statistical approaches for process monitoring(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Louifi, Abdelhalim; Harkat, Mohamed Faouzi(Directeur de thèse)Various data-driven approaches, such as Principal Component Analysis (PCA), are widely employed for process monitoring in industrial applications, particularly for detecting abnormal events. PCA-based Fault Detection and Isolation is a well-established strategy, praised for its robust performance. However, its reliability diminishes in uncertain systems where model uncertainties signi?cantly impact e ectiveness. To address this challenge, process modeling is conducted using PCA for interval-valued data, incorporating uncertainties directly into the modeling phase. Four of the most prominent methods for interval-valued PCA are detailed, alongside an extension of conventional PCAbased statistical process monitoring to handle interval-valued data. Over the past decade, this approach has garnered substantial research attention, leading to the development of multiple interval-valued PCA models. This thesis proposes a novel approach called Interval-Valued Principal Component Analysis (IV-PCA), designed to handle uncertainties by de?ning a safe interval for data ?uctuations. The developed technique is applied to the cement rotary kiln process and the Tennessee Eastman Process, where its performance is compared against conventional PCA and four leading Interval-Valued Data PCA (IVD-PCA) methods. Through tests involving actual involuntary system faults and various sensor faults, the IV-PCA demonstrates superior performance in accurately and quickly detecting distinct faults, even in stochastic environments with unknown and uncontrolled uncertainties. The results show signi?cant reductions in false alarms and missed detections compared to the best outcomes of the studied methodsItem Interval-valued statistical approaches for process monitoring(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Louifi, Abdelhalim; Harkat, Mohamed Faouzi(Directeur de thèse)Various data-driven approaches, such as Principal Component Analysis (PCA), are widely employed for process monitoring in industrial applications, particularly for detecting abnormal events. PCA-based Fault Detection and Isolation is a well-established strategy, praised for its robust performance. However, its reliability diminishes in uncertain systems where model uncertainties signi?cantly impact e ectiveness. To address this challenge, process modeling is conducted using PCA for interval-valued data, incorporating uncertainties directly into the modeling phase. Four of the most prominent methods for interval-valued PCA are detailed, alongside an extension of conventional PCAbased statistical process monitoring to handle interval-valued data. Over the past decade, this approach has garnered substantial research attention, leading to the development of multiple interval-valued PCA models. This thesis proposes a novel approach called Interval-Valued Principal Component Analysis (IV-PCA), designed to handle uncertainties by de?ning a safe interval for data ?uctuations. The developed technique is applied to the cement rotary kiln process and the Tennessee Eastman Process, where its performance is compared against conventional PCA and four leading Interval-Valued Data PCA (IVD-PCA) methods. Through tests involving actual involuntary system faults and various sensor faults, the IV-PCA demonstrates superior performance in accurately and quickly detecting distinct faults, even in stochastic environments with unknown and uncontrolled uncertainties. The results show signi?cant reductions in false alarms and missed detections compared to the best outcomes of the studied methodsItem Optimization of smart grid communication systems(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Grainat, Youcef; Recioui, Abdelmadjid(Directeur de thèse)This PhD research focuses on optimizing smart grid communication systems through the application of metaheuristic optimization algorithms, specifically Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), as well as advanced communication technologies such as Multiple-Input Multiple-Output (MIMO) and LoRa. The study aims to improve the reliability, efficiency, and security of real-time data exchange in critical smart grid components, including smart metering, home control, and Wide-Area Monitoring Systems (WAMS). In the first part, PSO and ACO are employed to optimize the placement of Data Aggregation Points (DAPs) in networks of 150 Z-wave smart meters deployed across various smart cities, with results showing that PSO provides faster execution, lower latency, and better cost-efficiency compared to ACO, especially in less complex networks. The second part introduces MIMO communication to improve data transmission accuracy and speed within WAMS, demonstrating performance gains in latency, data completeness, and correctness when compared with traditional systems. In the final phase, LoRa technology is utilized to support long-range, low-data-volume transmission for a proposed Wide-Area Network State Monitoring System. Using the IEEE 14-bus system with Phasor Measurement Units (PMUs), the study compares Single-Input Single-Output (SISO) and MIMO configurations under varying Signal-to-Noise Ratios (SNRs), revealing that MIMO significantly reduces the Bit Error Rate (BER) and that higher reporting rates further enhance data accuracy. Overall, the findings demonstrate the effectiveness of optimization and advanced communication techniques in building a more resilient, cost-effective, and high-performance smart grid communication infrastructureItem Control against faults of dynamical systems(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Azizi, Abdesamia; Kouadri, Abdelmalek(Directeur de thèse)Modern industrial systems have become increasingly complex, making them more vulnerable to faults in critical components such as actuators and sensors. The objective of this thesis is to develop robust control strategies capable of managing faults in actuators and sensors while maintaining the stability and desired performance of complex systems, even in the presence of faults and external disturbances. To achieve this, the work combines advanced fault estimation techniques with robust fault-tolerant control (FTC) strategies designed for both linear and nonlinear systems. Specifically, for linear systems, Unknown Input Observers (UIO) and output feedback fault-tolerant controllers are developed, with their gain matrices obtained by optimizing a multi-objective function using a genetic algorithm. Similarly, for nonlinear systems, UIO and output feedback sliding mode faulttolerant controllers are designed. Stability is ensured through the use of Linear Matrix Inequalities (LMIs) based on Lyapunov functions. Additionally, the LMI region is employed to control the poles of the overall closed-loop system, allowing for greater flexibility in achieving desired performance levels. The proposed framework not only detects and isolates faults but also compensates for them to ensure smooth operation. Simulations involving systems such as wind turbines and DC motors demonstrate the effectiveness of these methods. This research contributes to safer and more efficient industrial processesItem Interaction analysis and coupled vibrations control in rotary drilling systems(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Meddah, Sabrina; Idir, Abdelhakim(Directeur de thèse)This work focus on coupled vibration analyses of torsional-axial and torsional-lateral interactions control with a PID classic and an FOPID controller optimized by PSO for rotary drilling systems, oil and gaze field, in different Scenario. Where, the vibrations encountered during drilling operations are strongly coupled, resulting in complex effects on drilling performance. These vibrations can be categorized into three types based on their propagation directions within the drilling systems: axial, lateral, and torsional. While many researchers have individually studied each type of vibration, the robustness of developed controllers depends on considering the coupling effects of the other vibrations that are often overlooked. To ensure the robustness of such controllers, it is imperative to analyze the interaction effects of the control systems, specifically focusing on the torsional-axial and torsional-lateral interactions. The primary objective of this thesis is to investigate the interactions among these three types of vibrations and subsequently propose controllers for the coupled cases based on the interaction analysis results. The proposed contribution of interaction analysis was demonstrated through simulation studies under MATLAB, and FOPID controller was designed then optimized using PSO technique and RN. The deep analyses of obtained results demonstrated improved system controller robustness compared to previous studiesItem Analyse d’images radiographiques pour le diagnostic de la gonarthrose(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Messaoudene, Khadidja; Harrar, Khaled(Directeur de thèse)La gonarthrose est une pathologie dégénérative articulaire répandue qui altère considérablement la qualité de vie des patients. Un diagnostic précoce et précis est essentiel pour une prise en charge thérapeutique optimale. Cette recherche propose une approche novatrice combinant l'analyse texturale avancée et les méthodes d'apprentissage profond afin de développer un système intelligent d'aide au diagnostic. Notre étude se concentre sur la classification de la gonarthrose en deux stades : KL-0 (sain) et KL-2 (modéré). En ciblant ces stades précoces, nous visons à améliorer le diagnostic initial et à favoriser des interventions opportunes avant que la maladie ne progresse vers des formes plus sévères. Pour l'extraction des caractéristiques, nous avons exploré plusieurs méthodes basées sur la texture, notamment l'histogramme des gradients orientés (HOG), les motifs binaires locaux (LBP), les caractéristiques de Tamura, les filtres de Gabor, la matrice de cooccurrence de niveaux de gris (GLCM) et la transformée en ondelettes discrète (DWT). Par ailleurs, nous avons évalué l'extraction de caractéristiques par apprentissage profond en exploitant des modèles préentraînés via l'apprentissage par transfert, tels que GoogLeNet, ResNet-101, DenseNet-201, SqueezeNet et AlexNet. Face à la disponibilité limitée des données en imagerie du genou, nous avons testé différentes stratégies d'augmentation de données, incluant des transformations classiques (rotation, translation) ainsi que des approches avancées basées sur l'apprentissage profond, telles que les modèles génératifs et les autoencodeurs. Nous avons également mis en œuvre diverses techniques de sélection des caractéristiques, combinant des méthodes traditionnelles d'apprentissage automatique, comme l'analyse en composantes principales (ACP) et Relief, avec des approches issues de l'apprentissage profond, telles que la distillation des connaissances (KD) et les mécanismes d'attention. Les méthodes proposées ont été validées sur l'ensemble de données OAI (Osteoarthritis Initiative). Nous avons évalué notre approche sur différentes zones d'intérêt afin d'identifier les régions les plus touchées par l'arthrose, permettant ainsi une meilleure compréhension de la progression de la maladie. La méthodologie développée a atteint une performance élevée avec AUC = 98,3%, démontrant son efficacité dans la détection de l'arthrose. Le principal apport de cette recherche est un cadre unifié intégrant l'analyse texturale et les modèles d'apprentissage profond afin d'améliorer la fiabilité et la précision du diagnostic de la gonarthrose. Nos résultats montrent une amélioration significative des performances diagnostiques par rapport aux approches existantes, offrant ainsi des perspectives prometteuses pour une détection plus précoce et des interventions thérapeutiques mieux ciblées