Publications Scientifiques

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    Advanced Trajectory Planning Technique for Unmanned Underwater Vehicle Navigation with Enhanced Fuzzy Logic Control and Obstacle Avoidance Strategy
    (Springer Science and Business Media, 2025) Demim, Fethi; Saghor, Sofian; Belaidi, Hadjira; Rouigueb, Abdenebi; Messaoui, Ali Zakaria; Benatia, Mohamed Akram; Chergui, Mohamed; Nemra, Abdelkrim; Allam, Ahmed; Kobzili, Elhaouari
    Trajectory planning plays a pivotal role in Unmanned Underwater Vehicles (UUVs), and this study addresses this aspect by employing Rapidly-exploring Random Trees (RRT) and a Fuzzy Logic Control (FLC). The investigation focuses on utilizing the RRT algorithm for waypoint generation in static environments. Leveraging Particle Swarm Optimization (PSO) enhances UUV control by optimizing FLC parameters, ensuring trajectory adherence to obstacle avoidance criteria. Through diverse experimental scenarios, the efficacy of the FLC regulator has been demonstrated, particularly in 3D waypoint navigation using Line-Of-Sight (LOS) guidance, showcasing accurate waypoint navigation, precise course maintenance, and effective pitch and yaw angle control for successful destination arrival. Moreover, this study highlights the increasing importance of RANS simulations in comprehending flow dynamics. It emphasizes a CFD-centric approach for design enhancement and aims to simulate 3D turbulent flow around UUV using ANSYS CFX code. This simulation evaluates appendage effects on overall drag and their interaction with the hull, effectively characterizing hydrodynamic behavior around the defined shape, aligning with study objectives.
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    Enhanced quadrotor motion analysis through extended
    (SAGE, 2024) Saibi, Ali; Belaidi, Hadjira; Kobzili, Elhaouari; Demim, Fethi; Allam, Ahmed; Attouche, Nesrine; Belkacemi, Malak
    This paper aims to develop a stable quadrotor’s flight controller using the ESP32 microcontroller, enabling wireless guidance through a Graphical User Interface (GUI) on Raspberry Pi. In this context, an Extended Kalman Filter (EKF) with a nonlinear control model is employed to estimate the angular velocity and speed of the quadrotor. Simulationswere conducted using two nonlinear controllers (BackStepping Controller (BSC) and Sliding Mode Controller (SMC)) across various scenarios to prove the efficiency of the proposed method for trajectory tracking. The performance was analyzed using Root Mean Square Error (RMSE) and compared to previous works. It was noticed that in the both control scenarios, the estimated states followed the desired instructions, although a potential overshoot was noted in the case of SMC. Additionally, two control approaches were practically implemented. The first approach utilizes the classical Proportional Integral Derivative (PID) technique, while the second approach employs the modern Backstepping technique. The results demonstrate that both control approaches deliver satisfactory performance and responsiveness in stabilizing the quadrotor in real-world scenarios. However, it has been noticed that the Backstepping controller achieves higher performance, whereas, the PID controller’sperformance is constrained by tuning and model limitations.