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Öğe A Levenberg–Marquardt Learning-Based Artificial Neural Network Controller for Battery Charging in Hydrogen and Solar-Powered Electric Vehicle Stations(John Wiley and Sons Ltd, 2026) Özden, Mustafa; Ertekin, DavutGreen energy and renewable energy sources (RESs) are between the most important topics in power, energy, and transportation and are crucial for sustainability for next generations. When a hydrogen fuel cell or solar array is used for the electric vehicle (EV), the next step is using an efficient, high power, and simple structure based power electronics converter to storage the energy of these RESs into the battery pack. The integration of artificial intelligence into the control and optimization of DC–DC power converters presents promising opportunities in improving energy management and efficiency in EV sector. This study presents a low-input current and low voltage stress topology for application in fuel cell to battery charging systems in EVs. A current filter by forming a switched inductor cell at the input side of the converter guarantees a small ripple for input sources that enhances the longevity and long-life of the FC stacks or solar panels. The application of the switched capacitor circuits at the input and end sides of the converter decreases the voltage stress across the semiconductor devices and enhances the mean time to failure rate of the converter that is between the most important features of a converter. The presented topology enhances the input voltage to 7 and 19 times for duty ratios equal to 0.5 and 0.8, respectively, while the switch experiences three and nine times the input voltage for the same duty ratios, which is considerable. The configuration of the diodes and capacitors in the switched capacitor, by dividing the total voltage stress, results in impressively low voltage ripples. This converter includes one power switch, which minimizes the complexity of the controller and enhances the feasibility. The converter design incorporates a three-layer, three-input artificial neural network structure. The regression values were 0.982 for training, 0.983 for testing, and 0.9827 overall, indicating minimal prediction error and confirming the effective training of the neural network model. The laboratory test results for power levels around 200 W have been presented and confirm the correctness of the proposed algorithm and the application of the proposed converter. To obtain 0.5 A for the load under a 350 VDC output voltage, the input source presents an average current equal to 8 A, and the switch experiences around 160 V voltage stress across the drain–source pins. Results show that Inductor L2 has lower current stress than Inductor L1. © 2026 Wiley-VCH GmbH.Öğe Design and Vision-Based Control of a Low-Cost SCARA Robot(2025) Adar, Nurettin Gökhan; Özden, MustafaSCARA robots are widely used in industrial automation due to their high precision and speed, particularly in pick-and-place operations. In addition to conventional programming approaches, alternative vision-based control methods have gained interest to enhance flexibility and efficiency in robotic applications. This study presents the design and implementation of a Position-Based Visual Servoing (PBVS) for the SCARA robot system capable of detecting and manipulating objects in real-time. The proposed system consists of a fixed overhead camera, a SCARA robot, and Python-based control software. The software integrates image processing algorithms, kinematic calculations, and motor control, enabling the robot to autonomously identify objects, compute their positions, and execute pick and place tasks. To enhance object detection accuracy, Kuwahara filtering, Canny edge detection, morphological transformations, and connected component analysis were applied. Experimental results demonstrated that the combination of Kuwahara filtering and Canny edge detection achieved the lowest MSE error (8.45%), ensuring precise object localization. Furthermore, inverse kinematics was employed to generate accurate joint movements, allowing smooth and reliable grasping operations. The system was tested through 100 pick-and-place trials, achieving a 100% grasping success rate when Kuwahara filtering was applied. The experimental findings confirm that vision-based control significantly improves SCARA robot performance, making it suitable for automated assembly, material handling, and quality control applications.Öğe Enhanced grid stability and prolonging life span in renewable energy power converters using an advanced Sugeno-type AI-based neuro-fuzzy control(Springer Science and Business Media Deutschland GmbH, 2025) Özden, Mustafa; Ertekin, Davut; Baltacı, KübraA significant challenge lies in renewable energy sources incapacity to generate high voltages and their limited life spans when subjected to high-ripple conditions. This study introduces an innovative Sugeno-type neuro-fuzzy controller for an interleaved power converter configuration aimed at mitigating the input current ripples associated with these renewable energy sources, directly addressing the longevity concern controlled by an advanced neuro-fuzzy controller. The proposed converter employs a switched capacitor (SC) cell to amplify the generated voltage within the boost converter framework. Key attributes of the proposed converter include high voltage gain, enhanced efficiency and the utilization of short-duty ratio time intervals to minimize conduction power losses at elevated voltages. Furthermore, through interleaved configuration, the current ripple from the source is diminished while the SC cell concurrently amplifies the voltage gain. A Sugeno-type neuro-fuzzy control method, based on artificial intelligence, is employed for the proposed converter to drive the switches and produce an accurate output voltage. Since the converter is primarily built on a fuzzy controller, the proposed method is mathematically simple and easy to implement. The main contribution of the proposed control approach lies in the sampling of both the input and reference, as well as the output voltages, and the generation of precise duty cycles based on the sampled reference output voltage. Due to its capability of generating high voltages, the proposed converter and control system are suitable for use in DC grids and vehicle-to-grid applications. © The Author(s) 2025.Öğe Local Adaptive Phase Correction Algorithm For 3-D Profilometry Systems Based on Phase Shifting Method(Bursa Teknik Üniversitesi, 2022) Özden, MustafaThe method of reflecting sinusoidal phase-shifted patterns to the surface, based on the demodulation technique of phase information, has been a popular method to obtain 3-D surface depth using 2-D images. The phase information that is extracted with this method is wrapped; so it must be unwrapped. Even though the phase information is unwrapped, there will be some errors because of the possibility of non-sinusoidal characteristics of phase patterns, surface discontinuities, low sample rates, and technical handicaps (poor calibration and hardware malfunctions, and so on). To deal with these errors resulting from the phase unwrapping process, there are some computationally expensive and complex methods that have been presented. In this paper, a fast and low complex local adaptive phase correction algorithm based on the four-step phase shifting method is implemented. The method is firstly validated by using synthetic data. After the validation process, an optic test system is realized, and a few experiments are performed by using physical real data. For the optical system used to physically acquire the data, a lookup table-based calibration technique has also been developed to obtain accurate surface phase information. The performance of the method is evaluated with simulation results and real data, and visually compared to popular unwrapping methods.
