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    Balance Control of Brushless Direct Current Motor Driven Two-Rotor UAV
    (Mdpi, 2024) Cukdar, Ibrahim; Yigit, Tevfik; Celik, Hakan
    In this study, the balance control of a Brushless Direct Current Motor (BLDCM) driven Two-Rotor UAV (2R-UAV) was carried out. First, a MATLAB/Simulink model of the balance system of the 2R-UAV was built. Afterwards, classical and 2-DOF PID, and proposed Adaptive Fuzzy (AF) 2-DOF PID control structures were created on the STM32F4 microprocessor for both balance angle of the system and speed control of the BLDCMs. Classical and 2-DOF PID controller parameters were determined via Particle Swarm Optimization (PSO), a technique that is commonly used in control applications. For the balance control of the 2R-UAV, a Co-Simulation structure was created using the STM32F4 microprocessor and MATLAB/Simulink, and the performances of classical and 2-DOF PID, and AF 2-DOF PID controllers were examined comparatively. Upon examining the comparison results, it was found that the classical and 2-DOF PID, and AF 2-DOF PID stably controlled the balance of the 2R-UAV. The AF 2-DOF PID controller, proposed in this research, performed better than the classical and 2-DOF PID, especially under variable operating conditions.
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    BLDC Motor Driven, PSO Optimized 2-DOF PID Control of the Seesaw Balance System
    (Institute of Electrical and Electronics Engineers Inc., 2021) Cukdar, Ibrahim; Yigit, Tevfik; Celik, Hakan
    In this study, stability control of a 2-rotor unmanned aerial vehicle (UAV), for which various test studies have been continued recently for its use in the field of logistics, was carried out. Brushless Direct Current Motor (BLDC) used as drive element and Seesaw balance system in the study is modeled in Matlab/Simulink environment. The seesaw balance system is important for the analyzing to multi-rotor systems basically and being basis of the Synchropter UAV systems. The created 2-rotor Seesaw balance system model was run with both classical PID and two degrees of freedom (2-DOF) PID controllers and the results were compared. The parameters of 2-DOF PID and classical PID controllers used in the study were determined by Particle Swarm Optimization (PSO). When the results obtained from the simulation were examined, it was seen that the 2-DOF PID controller had better performance than the classical PID controller. © 2021 Chamber of Turkish Electrical Engineers.
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    Dimension Synthesis and Optimized FOPID Control of the Delta Robot with Moth Swarm Algorithm
    (Springer Heidelberg, 2023) Yigit, Tevfik; Celik, Hakan
    In this study, Delta robot that one of the most studied and used robot and has distinctive advantages and parallel structure, has been optimized in point of dimensions. 3D solid model of the optimized robot model transferred to MATLAB/Simulink environment. Dimensions of the Delta robot have been obtained to ensure desired workspace with minimum dimensions with moth swarm algorithm (MSA) optimization and particle swarm optimization (PSO). Widely known PSO algorithm has been used for the measure to the performance of the MSA algorithm that is new to the literature. Joints of the robot actuated with the brushless DC (BLDC) motors. System has been controlled fractional-order PID (FOPID) controller. Also integer-order PID controller (IOPID) has been used to clearly see the performance of the FOPID with the aim of comparison. Parameters of both of these controllers are optimized with MSA optimization in equal conditions. When the optimization cost results and the performance test results are examined, it is seen that the FOPID controller performed better than IOPID controller. Designed Delta robot system has been tested with different scenarios, and robotic system has been achieve that this scenarios with high performance.
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    Speed controlling of the PEM fuel cell powered BLDC motor with FOPI optimized by MSA
    (Pergamon-Elsevier Science Ltd, 2020) Yigit, Tevfik; Celik, Hakan
    In this study, Brushless DC (BLDC) motor, which is commonly used as a drive element in the unmanned aerial vehicle (UAV), electric vehicles, and mobile robots today, is powered by hydrogen technologies as environmentally friendly and controlled by a fractional-order PI (FOPI) controller structure. Proton Exchange Membrane (PEM) electrolyzer, PEM Fuel Cell (PEMFC), storage tank, BLDC motor, and motor driver system are modeled and integrated into the Simulink environment in MATLAB. PEM electrolyzer that is energized from the AC grid via the AC/DC converter generates the hydrogen. This generated hydrogen is stored in the storage tank and used by PEMFC to energize to the BLDC motor. The model of the BLDC motor is controlled by using a closed-loop FOPI controller for the variable speed and torque reference values. Parameters of the FOPI are determined by Moth Swarm Algorithm (MSA) optimization method. It is observed from the results that the PEMFC powered FOPI controlled BLDC motor operates stably at high performance for different speed and torque values as expected from the modern drive systems. Furthermore, it is seen that the required energy for the BLDC motor is provided by the PEMFC-PEM electrolyzer system without interruption and the FOPI controlled BLDC motor successfully follows the reference speed values for the different torque values. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.