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    Enhancing Dynamic Control and Stability Assessment of Cessna 172 Aircraft with a PID Controller for New Pilot Trainees
    (Springer, 2025) Dagal, Idriss; Erol, Bilal; Harrison, Ambe; Mbasso, Wulfran Fendzi; Ibrahim, AL-Wesabi
    This research proposes a novel approach to enhance the dynamic control and stability assessment of the Cessna 172 aircraft for new pilot trainees by incorporating a proportional-integral-derivative (PID) controller. The PID controller is designed to improve the aircraft's responsiveness to control inputs, reduce overshoot and settling time, and enhance overall stability. The study involves developing a mathematical model of the Cessna 172's longitudinal dynamics, designing a PID controller, and conducting simulations to evaluate the performance of the PID-controlled aircraft. The evaluation focuses on key metrics such as stability, responsiveness, overshoot, and settling time. The results of the study demonstrate that the PID controller effectively enhances the dynamic control and stability of the Cessna 172, providing new pilot trainees with a safer and more efficient learning experience. The PID controller's ability to mitigate the effects of pilot errors and disturbances contributes to improved flight performance and reduced risk of accidents. Future research directions include exploring the use of adaptive PID controllers, integrating PID controllers with other advanced flight control systems, and conducting flight tests to validate the performance of the PID-controlled Cessna 172 in real-world conditions.
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    Leveraging a novel grey wolf algorithm for optimization of photovoltaic-battery energy storage system under partial shading conditions
    (Pergamon-Elsevier Science Ltd, 2025) Dagal, Idriss; Ibrahim, AL-Wesabi; Harrison, Ambe
    Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm Optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.