Browsing by Author "Mohamed Benghanem"

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    Novel Nonlinear PI Controller Using Metaheuristic Algorithms for Speed Control of Wind Turbine Systems
    (Journal Européen des Systèmes Automatisés Vol. 58, No. 8, August, 2025, pp. 1593-1608, 2025-08-31) Marwa Arabi; Youcef Zennir; Hichem Bounezour; Mohamed Benghanem; J.E.S. Garcia; M. Wadi
    Wind turbines operate under highly dynamic conditions influenced by unpredictable wind profiles and external disturbances. The nonlinear characteristics of their dynamic models further complicate their modeling and control. This research focuses on optimizing the power output of a Wind Energy Conversion System (WECS) equipped with a Permanent Magnet Synchronous Generator (PMSG). To achieve this, a Maximum Power Point Tracking (MPPT) strategy is developed, integrating an innovative nonlinear PI controller. The parameters of this controller are fine-tuned using advanced meta-heuristic optimization techniques, including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Golden Jackal Optimization (GJO). Simulation results highlight the superior performance of the GJO-NLPI controller, demonstrating exceptional accuracy and rapid response in regulating mechanical rotation speed, while effectively reducing overshoot. The proposed control architecture showcases significant advancements in power extraction efficiency and dynamic performance.
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    Reinforcement learning for hexapod robot trajectory control: a study of Q-learning and SARSA algorithms
    (International Journal of Intelligent Robotics and Applications, 2025-10-25) Ahmed Benyoucef; Youcef Zennir; Ammar Belatreche; Manuel F. Silva; Mohamed Benghanem
    Hexapod robots, with their six-legged design, excel in stability and adaptability on challenging terrain but pose signifi cant control challenges due to their high degrees of freedom. While reinforcement learning (RL) has been explored for robot navigation, few studies have systematically compared on-policy and off-policy methods for multi-legged locomo tion. This work presents a comparative study of SARSA and Q-Learning for trajectory control of a simulated hexapod robot, focusing on the influence of learning rate (α), discount factor (γ), and eligibility trace (λ). The evaluation spans eight initial poses, with performance measured through lateral deviation (Ey), orientation error (Eθ), and iteration count. Results show that Q-Learning generally achieves faster convergence and greater stability, particularly with higher γ and λ values, while SARSA can achieve competitive accuracy with careful parameter tuning. The findings demonstrate that eligibility traces substantially improve learning precision and provide practical guidelines for robust RL-based control in multi-legged robotic systems.