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    A unified robust hybrid optimized Takagi-Sugeno fuzzy control for hydrogen fuel cell-integrated microgrids
    (Pergamon-Elsevier Science Ltd, 2025) Ozcan, Omer Faruk; Kilic, Heybet; Ozguven, Omerul Faruk
    Microgrids integrating renewable energy sources, hydrogen fuel cells, battery-based energy storage systems (ESS), and various loads have become essential for the seamless incorporation of distributed energy into the grid. Hydrogen fuel cells, in particular, are crucial for providing reliable, clean electricity, especially during periods of reduced renewable energy availability. This paper presents a unified control solution for converters and inverters, utilizing a hybrid optimized Takagi-Sugeno-Kang (TSK) fuzzy-based approach to manage ESS operation, with a strong focus on hydrogen fuel cells. The strategy dynamically controls the power generated or stored in the ESS, prioritizing hydrogen fuel cells based on grid demand, available renewable power, and the battery's state of charge (SOC). This method reduces active power exchange at the point of common coupling during grid-connected mode and supports frequency regulation during island mode operations, thereby improving system stability and efficiency. To enhance Fuzzy System (FS) design, a hybrid genetic algorithm (GA) and grey wolf optimizer (GWO) approach is applied, accelerating rule generation and optimizing system performance. Simulation results demonstrated that the proposed hybrid GGWO-TSK control strategy achieved 97.58% PV and 98.56% wind tracking efficiency, while optimizing hydrogen fuel cell utilization to maintain a 98.88% fuel cell tracking efficiency. This method effectively minimized power exchange, improved frequency regulation, and enhanced microgrid stability, ensuring efficient energy management in both grid-connected and islanded modes. The proposed framework proves to be a robust and scalable solution for hydrogen fuel cell-integrated microgrids, contributing to a more resilient and sustainable energy system under diverse operating scenarios.
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    Design of Adaptive Fractional-Order PID Controller to Enhance Robustness by Means of Adaptive Network Fuzzy Inference System
    (Springer Heidelberg, 2017) Arpaci, Huseyin; Ozguven, Omerul Faruk
    In this paper, a tuning strategy for the design of fractional-order proportional-integral-derivative ((PID mu)-D-lambda) controllers is proposed. First, a (PID mu)-D-lambda controller is designed with genetic algorithm in order to obtain the training data. Then, three Adaptive Network Fuzzy Inference System (ANFIS) structures, related to K-p, K-i and K-d parameters of the (PID mu)-D-lambda controller, are formed by using the training data. These ANFIS structures are used in the (PID mu)-D-lambda controller instead of K-p, K-i and K-d parameters, and they are capable of self-tuning during the simulation based on the input signal of the adaptive (PID mu)-D-lambda controller (ANFIS-(PID mu)-D-lambda). Finally, in order to show the control performance and robustness of the proposed parameters adjustment method with ANFIS, simulation results are obtained by using the MATLAB-Simulink program for two different systems and the results obtained from ANFIS-(PID mu)-D-lambda controller are compared with the results of (PID mu)-D-lambda and fuzzy logic controller.
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    Fuzzy PID Control by Grouping of Membership Functions of Fuzzy Antecedent Variables with Neutrosophic Set Approach and 3-D Position Tracking Control of a Robot Manipulator
    (Springer Singapore Pte Ltd, 2018) Can, Mehmet Serhat; Ozguven, Omerul Faruk
    This paper aims to design of the neutrosophic fuzzy-PID controller and it has been compared with the conventional fuzzy-PID controller for position tracking control in terms of robustness. In the neutrosophic fuzzy-PID controller, error (e) and change of error (ce) were assessed separately on two fuzzy inference systems (FISs). In this study, the designed method is different from the conventional fuzzy logic controller design, membership degrees of antecedent variables were determined by using the T(true), I(indeterminacy), and F(false) membership functions. These membership functions are grouped on the universe of discourse with the neutrosophic set approach. These methods were tested on three-dimensional (3-D) position-tracking control application of a spherical robot manipulator in the MATLAB Simulink. In all tests, reference trajectory was defined for movements of all axes of the robot manipulator. According to the results of the study, when the moment of inertia of the rotor is changed, less overshoot ratio and less oscillation are obtained in the neutrosophic fuzzy-PID controller. Thus, our suggested method is seen to be more robust than the fuzzy-PID controllers.
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    Intelligent optimized load shedding under renewable and load uncertainties in fuel cell-integrated islanded microgrids\
    (Pergamon-Elsevier Science Ltd, 2026) Ozcan, Omer Faruk; Kilic, Heybet; Ozguven, Omerul Faruk
    The stochastic nature of renewable energy sources and load demand poses significant challenges to maintaining voltage and frequency stability in islanded microgrids. To address these challenges, this paper proposes an adaptive voltage-frequency control framework based on a Genetic-Gray Wolf Optimized interval Type-II Sugeno fuzzy logic controller. The proposed system integrates a hydrogen fuel cell into a hybrid microgrid that considers multi-source uncertainties on both the generation and demand sides. In this configuration, shortterm fluctuations in renewable energy generation are compensated by the battery energy storage system, while the fuel cell provides long-term power support, ensuring system sustainability and stability. Renewable and load variations are modeled using probabilistic distributions, and a roulette wheel mechanism dynamically selects one of 20 stochastic scenarios to represent various uncertainty conditions. The proposed GGWO-Type II fuzzy controller is evaluated under four operating scenarios, including manual and optimized demand response programs. The simulation results demonstrate that it outperforms conventional P/F and Q/V droop and Type-I fuzzy controllers, achieving superior voltage-frequency regulation and faster transient recovery under uncertainty. In the optimized DRP case , the system achieved the fastest dynamic response and the lowest voltage and frequency deviations (Vmin = 0.9685 p.u., fmin = 59.397 Hz). Compared to the reference uncertainty scenario, the proposed controller improved voltage and frequency regulation by approximately 17% and 19%, respectively, while further improvements of 8% and 10% were observed relative to the manual DRP case. These results confirm that hydrogen fuel cell integration, combined with the optimized control strategy, significantly enhances the dynamic stability and resilience of the islanded microgrid under uncertain operating conditions.
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    Modelling and optimization of RSM and ANN techniques in treatment of real textile wastewater using Mn-doped Fe3O4/zeolite as heterogeneous catalyst
    (Elsevier, 2025) Ozguven, Ayse; Kaya, Zilan; Ozguven, Omerul Faruk; Ozturk, Dilara
    This study explored the use and effectiveness of Mn-doped Fe3O4/zeolite as a catalyst in removing COD from real textile wastewater through the HF process, using ANN and RSM techniques. The catalyst was characterized by SEM, EDX, BET, XRD, FTIR, and XPS. The catalyst structure was polycrystalline and microporous, with an average pore diameter estimated as 11.24 nm. BET surface area and pore volume of the catalyst were found as 8.9599 m(2)/g and 0.0321 cm(3) /g. Optimum operation conditions are determined as: 1 g/L catalyst dosage, 2 g/L H2O2, 59.9 degrees C temperature, 244.7 rpm shaking speed, pH 2.5, and 99.4 min reaction time. The catalyst showed good performance, achieving 87.6 % and 92.5 % COD and color removal under optimum conditions. center dot OH played a key role and was a dominant oxidative species in removing pollutants. Scavenging of center dot HO2 caused a 17.6 % and 17.5 % decrease in COD and color removal. The removal performances were best described by the BMG kinetic model (R-2 > 0.99). The catalyst can be reused for five cycles, achieving at least 65 % COD removal and 80 % color removal. This study demonstrates that Mn-Fe3O4/zeolite coupled with ANN optimization offers a reliable and sustainable approach for treating real textile effluents.
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    PID Tuning with Neutrosophic Similarity Measure
    (Springer, 2017) Can, Mehmet Serhat; Ozguven, Omerul Faruk
    In this paper, a method for adjusting the proportional-integral-derivative (PID) coefficients based on the neutrosophic similarity measure is proposed. First, rough PID coefficients were determined by the Ziegler-Nichols method, and the upper and lower limit values for the search range of the PID coefficients were determined. At each step of the search range, we applied a unit step function to the system and obtained the transient response characteristics. The obtained values were converted into a neutrosophic set (real set) by using defined membership functions. Then, the optimal PID coefficients were obtained using the similarity ratio between the real and ideal (target) neutrosophic sets. In calculating the similarity ratio, the Hamming, Euclidean, Set-theoretic, Jaccard, and Dice approaches were applied, and the results were compared. Finally, the proposed method was tested on two transfer functions, and it was demonstrated that the proposed method can be used to adjust PID coefficients.