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Öğe Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response(Korean Institute of Electrical Engineers, 2024) Katipoğlu, Deniz; Soylu, SelimThis study comprehensively investigates modeling and controller design for two-area load frequency control (LFC) system with demand response (DR) and time delays. An optimized fractional order proportional-integral (FOPI) controller is designed both to improve the stability of grid frequencies and to deal with the time delays. Using the stability boundary locus (SBL) method, the stability region for the FOPI controller parameter space is obtained. The boundaries of these stability regions are used in the optimization process of the FOPI controller parameters. A new objective function is used to minimize the integral time-weighted absolute error (ITAE) and time domain specifications such as overshoot (OS) and undershoot (US) of the frequency and tie-line power deviations. The parameters of the FOPI controller are optimized by Artificial Hummingbird Algorithm (AHA). The simulation results are compared with gray wolf optimizer (GWO) and particle swarm optimization (PSO) tuned FOPI controllers. The AHA tuned FOPI (AHA-FOPI) controller gives very promising results, especially in terms of improving the transient response of the system by reducing the settling time (ST) and steady-state error (ess). Additionally, the effectiveness of the AHA-FOPI is demonstrated under increased time delay. It is evident from the simulation results that the AHA-FOPI controller has successfully improved the time domain characteristics of the LFC-DR system.Öğe In silico testing of optimized Fuzzy P plus D controller for artificial pancreas(Elsevier, 2018) Soylu, Selim; Danışman, KenanBackground and objectives Despite therapeutic advances, a complete cure has not been found yet for patients with type 1 diabetes (T1D). Artificial pancreas (AP) is a promising approach to cope with this disease The controller part of the AP can compute the insulin infusion rate that keeps blood glucose concentration (BGC) in normoglycemic ranges Most controllers rely on model-based controllers and use manual meal announcements or meal detection algorithms. For a fully automated AP, a controller only using the patient's BGC data is needed Methods An optimized Mamdani-type hybrid Fuzzy P+D controller was proposed Using the University of Virginia/Padova Simulator, a 36 h scenario was tested in nine virtual adult patients To take into account the effect of continuous glucose monitor noise, the scenario was repeated 25 times for each adult The mam outcomes were the percentage of time BGC levels in the euglycemic range, low blood glucose index (LBGI), and blood glucose risk index (BGRI), respectively Results The obtained BGC values were found to be in the euglycemic range for 82.6% of the time Moreover, the BGC values were below 50 mg/dl, below 70 mg/dl and above 250 mg/dl for 0%, 0.35% and 0.74% of the time, respectively The BGRI, LBGI, and high blood glucose index (HBGI) were also found as 3.75, 0.34 and 3.41, respectively The proposed controller both increases the time the BGC levels in the euglycemic range and causes less hypoglycemia and hyperglycemia relative to the published techniques studied in a similar scenario and population. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences Published by Elsevier B.V. All rights reserved.Öğe Optimal fuzzy P + Dµ controller for cancer chemotherapy(Elsevier Ltd, 2024) Ay, Sena; Soylu, SelimThis paper aims to develop a robust, effective, and optimal controller for an automatic intravenous drug delivery system for the chemotherapy treatment of cancer patients. For this purpose, a novel hybrid control method, namely the Fuzzy P + Dµ controller, is designed by combining fractional calculus and fuzzy logic controller (FLC). Particle swarm optimization (PSO) algorithm is employed to tune the controller parameters optimally. To evaluate the performance of the proposed controller, a cancer patient model that considers cumulative drug toxicity, one of the most common side effects of chemotherapy, is utilized. Limits on toxicity and drug concentration for patient safety are incorporated into the controller design and optimization process. The graphical and numerical simulation results show that almost no cancerous cells are left at the end of the treatment period. The effectiveness of the controller is proven by the performance index (PI) obtained. Our optimal hybrid Fuzzy P + Dµ outclasses other control techniques in previous related studies with the best PI of 44.426 and the minimum final number of cancerous cells (FNCCs) of 5.084E-08. Moreover, in robustness tests perturbing intra-patient parameters and using different treatment protocols, the cumulative drug toxicity level is kept within safe limits, and almost no cancerous cells remainÖğe Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm(SAGE Publications Ltd, 2023) Çelik, Muhammet; Soylu, SelimThis study represents a comprehensive investigation of the performance of Artificial Hummingbird Algorithm for parameter estimation for Polymer Electrolyte Membrane Fuel Cell. With this purpose, four commercial fuel cell systems which were widely preferred in the literature such as NedStack PS6 (Case-I), 250 W fuel cell stack (Case-II), Horizon 500 W (Case-III), and BCS 500 W (Case-IV) were chosen. In order to compare the performance of this algorithm, seven well-known optimization techniques including Artificial Bee Colony, Salp Swarm Optimization, Particle Swarm Optimization, Gray Wolf Optimization, Genetic Algorithm, Harris Hawks Optimization, and Whale Optimization Algorithm were used. The sum of the squared errors, computational speed, and statistical measurements were calculated for the performance comparison. In this context, the best SSE values were found as 2.06556, 5.25017, 0.02477, 0.01170 for Case-I, Case-II, Case-III, and Case-IV, respectively. The best standard deviation value was found as 1e?6 for the Case-III. Based on the obtained results, the Artificial Hummingbird Algorithm established itself as a competitive optimization technique for parameter estimation study of PEMFC in terms of computational speed and robustness.Öğe Performance Analysis of Fractional-Order Controllers for Blade Pitch Angle Control of a Wind Turbine(Institute of Electrical and Electronics Engineers Inc., 2024) Soylu, SelimDesigning an optimal blade pitch angle controller for a wind turbine (WT) presents a critical engineering challenge, affecting the turbine's reliability, safety, and power output efficiency. This research investigates the blade pitch angle controller design for a 500 kW WT using five different fractional-order controllers (FOCs) in a simulated environment. To optimize the parameters of these FOCs, a bio-inspired metaheuristic method known as the artificial hummingbird algorithm (AHA) is employed. A fitness function is determined based on the error between nominal output power and measured output power of the WT, and an optimization process is performed to minimize this fitness function. For reliability of the optimization process, 30 independent runs are performed, and their statistical data is recorded. Following the optimization process, the power output and time response characteristics of the WT system are evaluated by simulation studies to determine their performance. The simulation results indicate that the system controlled with the fractional-order proportional-derivative (FOPD) controller achieves the best transient response characteristics with a maximum overshoot of 1.104%, maximum undershoot of 2.491% and settling time of 0.037 seconds and outperforms the other four FOCs. ©Öğe Performance Comparison of Metaheuristic Algorithms on FOPID-Controlled Anti-Cancer Drug Delivery System(Institute of Electrical and Electronics Engineers Inc., 2023) Ay, Sena; Soylu, SelimChemotherapy is one of the most effective and preferred treatment methods in cancer treatment. It is essential to finely adjust chemotherapy drug doses for the patient's survival and comfort in terms of side effects during the treatment process. In this study, a well-known anti-cancer drug scheduling patient model was used. This patient model was controlled by an optimized fractional order proportional-integral-derivative (FOPID) controller. In the optimization process five different swarm-based metaheuristic algorithms such as AHA, GWO, PSO, SSA, and WOA were used. To compare their performances, each algorithm was run independently 30 times and statistical data was collected. As a result of the simulation studies, the GWO-based optimal FOPID (GWO-FOPID) and PSO-based optimal FOPID (PSO-FOPID) controllers outperformed with the final number of cancer cells (CCs) of 35.253 and 35.255, respectively. Moreover, the highest performance index of 24.07 was achieved for these controllers.
