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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Izci, Davut
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Parameter Extraction of Triple-diode Photovoltaic Model via RIME Optimizer with Neighborhood Centroid Opposite Solution Izci, Davut; Ekinci, Serdar; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22256

Abstract

In this investigation, a novel application of the RIME optimizer with neighborhood centroid opposite solution is introduced to robustly estimate parameter values for an accurate photovoltaic triple-diode model. The suggested optimizer's performance is rigorously evaluated in comparison to other well-documented methods. The evaluation of the proposed optimizer is conducted using real data from the RTC France solar cell, and the results are assessed through various evaluation metrics, including root mean square error and statistical analyses for multiple independent runs. Specifically, the proposed optimizer demonstrates superior performance by achieving the lowest objective function values compared to other algorithms. Through a comprehensive quantitative and qualitative assessment, it can be inferred that the estimated parameters of the triple-diode model obtained using the proposed optimizer surpass the accuracy of those acquired through other optimization algorithms under consideration.
Aircraft Pitch Control via Filtered Proportional-Integral-Derivative Controller Design Using Sinh Cosh Optimizer Abualigah, Laith; Ekinci, Serdar; Izci, Davut
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1433

Abstract

An innovative approach to controlling aircraft pitch is shown in this research. This approach is accomplished by adopting a proportional-integral-derivative with filter (PID-F) mechanism. A novel metaheuristic approach that we propose is called the sinh cosh optimizer (SCHO), and it is intended to further optimize the settings of the PID-F controller that is used in the aircraft pitch control (APC) configuration. An in-depth comparison and contrast of the recommended method is carried out, and statistical and time domain assessments are utilized in order to ascertain the success of the method. When it comes to managing the APC system, the SCHO-based PID-F controller delivers superior performance compared to other modern and efficient PID controllers (salp swarm based PID, Harris hawks optimization based PID, grasshopper algorithm based PID, atom search optimization based PID, sine cosine algorithm based PID, and Henry gas solubility optimization based PID) that have been published in the literature. When compared to alternative approaches of regulating the APC system, the findings demonstrate that the way that was presented is among the most successful as better statistical (minimum of 0.0033, maximum of 0.0034, average of 0.0034 and standard deviation of 5.1151E−05) and transient response (overshoot of 0%, rise time of 0.0141 s, settling time of 0.0230 s, peak time of 0.0333 s and steady-state error of 0 %) values have been achieved.
Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller Abualigah, Laith; Izci, Davut; Ekinci, Serdar; Zitar, Raed Abu
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1347

Abstract

The precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach employing the artificial rabbits optimizer (ARO) for tuning a PID controller with a filtering mechanism (PID-F) in aircraft pitch control systems. This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. Simulation results validate the stability and consistency of the proposed ARO/PID-F approach. Comparative analysis with various optimization algorithm-based controllers from the literature demonstrates the effectiveness of the proposed technique. Specifically, the ARO/PID-F controller exhibits a rapid response, zero overshoot, minimal settling time, and precise control during critical phases. The obtained results position the proposed methodology as a promising and innovative solution for optimizing aircraft pitch control systems, offering improved performance and reliability.
Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System Ekinci, Serdar; Eker, Erdal; Izci, Davut; Smerat, Aseel; Abualigah, Laith
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1644

Abstract

This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems.
Nelder-Mead Enhanced Gazelle Optimizer for Solving Complex Optimization Problems Yağız, Beytullah; Atar, Şeyma Nur; Eker, Erdal; Ekinci, Serdar; Izci, Davut
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.240

Abstract

This paper presents the improved gazelle optimization algorithm, which is a new approach in the field of metaheuristic optimization algorithms inspired by nature. By hybridizing the classical gazelle optimization algorithm with the Nelder-Mead simplex method, the improved gazelle optimization algorithm was developed. The proposed IGOA algorithm aims to combine GOA's global search capability with NM's local healing power to provide a more balanced and effective optimization of optimization problems. The performance of the algorithm was evaluated by 30 independent runs on the CEC2017 benchmark functions. The statistical results obtained from the analyses of the mean, standard deviation, best and worst values and Wilcoxon signed ranks test show that IGOA exhibits a superior or competitive performance compared to other current optimization algorithms. Furthermore, the boxplot and convergence curves revealed that IGOA exhibited stable convergence behavior and had a low tendency to get stuck at local optimums. Big-O analysis, on the other hand, confirmed that the algorithm can scale efficiently even in high-dimensional problems. The results prove that the IGOA algorithm is a highly competitive, effective and generalizable tool in solving complex optimization problems.
Co-Authors Abualigah, Laith Alfian Ma’arif Atar, Şeyma Nur Eker, Erdal Ekinci, Serdar Smerat, Aseel Yağız, Beytullah Zitar, Raed Abu