Browsing by Author "Hadid, Samira"

Now showing 1 - 4 of 4

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.
Commande adaptative d’une machine asynchrone en utilisant différentes structures d’estimateurs (non-Linéaire et neuronal)
(2009) Hadid, Samira
Les techniques de contrôle intelligentes telles que : les réseaux de neurones artificiels sont souvent utilisés pour résoudre des problèmes de non-linéarité et de variations paramétriques. Grâce à leur capacité d’apprentissage, les réseaux de neurones artificiels peuvent donner de bons résultats de contrôle du MAS, car ils peuvent s’adapter aux non linéarités du système, ainsi qu'aux perturbations et aux variations paramétriques. Le but de ce mémoire est la mise en œuvre d'une loi de commande non linéaire adaptative de haute performance pour un moteur asynchrone, avec comme objectifs : améliorer la poursuite de trajectoires, garantir la stabilité, la robustesse aux variations des paramètres et le rejet de perturbations. L'élément de base dans une commande adaptative est le modèle pour adapter le comportement du système. Deux méthodes sont utilisées pour la conception du modèle d’adaptation : l'une utilise les outils mathématiques de la géométrie différentielle, et les règles d’adaptation classiques pour la commande non linéaire adaptative, et l'autre est une conception à partir de réseaux de neurones pour une commande adaptative neuronale. Les performances obtenues, de poursuite de trajectoires, de robustesse aux variations de paramètres et de rejet de perturbation, sont améliorées en proposant deux estimateurs adaptatifs universels: non linéaire et neuronal, permettant ainsi, d’estimer les déviations des paramètres du MAS. Nous croyons que ce que nous avons réalisé avec la commande adaptative neuronale non linéaire constitue une contribution au domaine des entraînements à vitesse variable par machines asynchrones
Enhancing quadcopter autonomy: implementing advanced control strategies and intelligent trajectory planning
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Hadid, Samira; Boushaki, Razika; Boumchedda, Fatiha; Merad, Sabrina
In this work, an in-depth investigation into enhancing quadcopter autonomy and control capabilities is presented. The focus lies on the development and implementation of three conventional control strategies to regulate the behavior of quadcopter UAVs: a proportional–integral–derivative (PID) controller, a sliding mode controller, and a fractional-order PID (FOPID) controller. Utilizing careful adjustments and fine-tuning, each control strategy is customized to attain the desired dynamic response and stability during quadcopter flight. Additionally, an approach called Dyna-Q learning for obstacle avoidance is introduced and seamlessly integrated into the control system. Leveraging MATLAB as a powerful tool, the quadcopter is empowered to autonomously navigate complex environments, adeptly avoiding obstacles through real-time learning and decision-making processes. Extensive simulation experiments and evaluations, conducted in MATLAB 2018a, precisely compare the performance of the different control strategies, including the Dyna-Q learning-based obstacle avoidance technique. This comprehensive analysis allows us to understand the strengths and limitations of each approach, guiding the selection of the most effective control strategy for specific application scenarios. Overall, this research presents valuable insights and solutions for optimizing flight stability and enabling secure and efficient operations in diverse real-world scenarios.
Linear and nonlinear control design for a quadrotor
(2025) Hadid, Samira; Boushaki Zamoum, Razika; Refis, Youcef
In the current study, the quadrotor's nonlinear dynamic model is developed using the Newton-Euler approach. Following that, several nonlinear and linear control strategies for tracking the quadrotor's trajectory are applied. First, by employing distinct controllers for each output variable, direct application of the linear proportional integral derivative (PID) controller to the nonlinear system is realized. This system may also be linearized about an operational point to generate linear controllers, according to the linear quadratic regulator (LQR) demonstration. Nevertheless, in practice, the system dynamics may not always be accurately reflected by this linear approximation and may even be relatively wasteful. Nonlinear regulators, including the feedback linearization (FBL) controller, sliding mode controller (SMC), and modified sliding mode controller (MSMC), perform better in such situations. The trajectory tracking capabilities, dynamic performance, and potential disruption impact of both methods are evaluated and compared. The FBL with LQR was the best controller among them all. The SMC and the MSMC were also very good in tracking the trajectory.