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    Altitude Control of Quadrotor Based on Metaheuristic Methods
    (2025) Kurnaz, Muhammed Kıvanç; umay, Yagmur; KOCA, GONCA OZMEN
    Quadrotor, which is used in many fields and is still a challenge to control, has a complex kinematic and dynamic system, and its flight performance depends on many variables that need to be controlled simultaneously. In this study, the effective determination of PID parameters for altitude control of quadrotors, which presents a complex control problem, has been tested comparatively with innovative metaheuristic approaches. Among the strong metaheuristic algorithms, Crow Search Algorithm (CSA), Particle Swarm Optimization Algorithm (PSO), Golden Jackal Optimization Algorithm (GJO), and Jellyfish Search Algorithm (JSA) were comparatively analyzed for the determination of PID parameters. The parameters obtained with CSA caused the minimum steady-state error with the value of 6.9580e-04 in the closed-loop control system. A minimum overshoot was also obtained with the parameters optimized with CSA. When these results are evaluated, it can be said that CSA performs better than other altitude control algorithms, considering the quadrotor's stable and accurate positioning performance.
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    Recent metaheuristics on control parameter determination
    (2025) umay, Yagmur; KOCA, GONCA OZMEN; Akpolat, Zuhtu Hakan
    Metaheuristics have been widely used in recent years for tuning control parameters since they have a simple structure, are easy to apply, and provide efficient solutions. In this study, control of a two-wheeled mobile robot using the inverted pendulum principle is proposed. The performances of nine recent metaheuristics (Political Optimizer, Equilibrium Optimizer, Aquila Optimizer, Flow Directional Algorithm, Cheetah Optimizer, Golden Jackal Optimizer, Artificial Rabbit Optimization, Gazelle Optimizer, and Pelican Optimization) have been investigated for the balancing and speed control of a two-wheeled vehicle. In this context, a framework consisting of two cascaded PI controllers is designed to provide balance and speed control of the two-wheeled vehicle. The performances of the recent metaheuristics are also compared with previously introduced effective metaheuristic algorithms for further evaluation. The parameters of the controllers are tuned by using these metaheuristics. In experimental studies, quantitative and qualitative analyses are performed for evaluation of the metaheuristics. The dynamic system properties, convergence curves, computational times, and statistical results are provided to prove optimal control performances. The results show that 11 out of 14 compared algorithms produce similar optimal results in speed and balance control of the two-wheeled vehicle. The rest of them do not provide satisfactory results for the tuning of optimum control parameters of the two-wheeled vehicle.