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Dergiler / Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 6 Sayı: 1

Geliştirilmiş Gri Kurt Optimizasyon Algoritmasına Dayalı Kesirli Mertebeden Oransal İntegral Türevsel PID Denetleyici Tasarımı

Improved Gray Wolf Optimization Algorithm for Tuning Non-integer Order Proportional Integral Derivative Controller Design

Kadri Doğan(ARTVIN CORUH UNIVERSITY)0000-0002-6622-3122
Hasan Başak(ARTVIN CORUH UNIVERSITY)0000-0002-3724-6819
Sayfa
220–244
Yayın Tarihi
2025-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Değerlendirme sonuçları GWOJOS'un performansının GWO, SMA, ASO, SA ve OBLSMASA algoritmaların performansından daha iyi olduğunu göstermiştir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu çalışmada, doğru akım (DC) motorunun hızını kontrol etmek için tam sayı olmayan mertebeden oransal-integral-türevsel (NIOPID) kontrolör kullanılmıştır.
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Temel Bulgular & Veri:JOS, Dinamik Karşıtlık (DO) ve Seçici Lider Karşıtlık (SLO) olmak üzere iki karşıtlık stratejisinin bir araya getirilmesiyle karşılıklı bir güçlendirme sağlar.
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Sonuç & Katkı:Değerlendirme sonuçları GWOJOS'un performansının GWO, SMA, ASO, SA ve OBLSMASA algoritmaların performansından daha iyi olduğunu göstermiştir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:NIOPID · DC Motor · GWOJOS

Özet

Bu çalışmada, doğru akım (DC) motorunun hızını kontrol etmek için tam sayı olmayan mertebeden oransal-integral-türevsel (NIOPID) kontrolör kullanılmıştır. Kontrolör parametreleri, GWO algoritması ve yeni tanımlanan JOS özelliğinin birleşiminden oluşan GWOJOS algoritması kullanılarak optimum şekilde ayarlanmıştır. JOS, Dinamik Karşıtlık (DO) ve Seçici Lider Karşıtlık (SLO) olmak üzere iki karşıtlık stratejisinin bir araya getirilmesiyle karşılıklı bir güçlendirme sağlar. DO ve SLO, belirli bir arama uzayında sırasıyla keşif ve sömürü dengesini iyileştirir. Optimizasyon aşamasında JOS, SLO'yu kullanarak GWO'nun hedefe hızlı bir şekilde saldırmasına yardımcı olur. DO, GWO'nun en uygun avı bulmak için daha fazla fırsat bulmasına yardımcı olur. GWO, JOS ile performansını artırabilmektedir. Bu birleşim, GWO'nun yakınsama oranını hızlandırmaya yardımcı olur. GWOJOS'un performansını CEC2017'deki kıyaslama fonksiyonlarını kullanarak değerlendirdik. Kıyaslama bileşim, hibrit, multimodal ve unimodal fonksiyonları kapsamaktadır. GWOJOS algoritmasını kullanan DC-motor için NIOPID tabanlı hız kontrol sistemi, performans kriterlerini (maksimum aşım, kararlı durum hatası, yükselme süresi ve yerleşme süresi) dikkate alan bir zaman alanı amaç fonksiyonu kullanılarak tasarlanmıştır. Önerilen yeni yaklaşımın performansını değerlendirmek için sağlamlık, zaman ve frekans alanı simülasyonları dahil olmak üzere bazı analizler kullanılmıştır. Değerlendirme sonuçları GWOJOS'un performansının GWO, SMA, ASO, SA ve OBLSMASA algoritmaların performansından daha iyi olduğunu göstermiştir.

Abstract

In this study, a noninteger-order proportional–integral–derivative (NIOPID) controller was used for controlling the speed of the direct current (DC) motor. The controller parameters have optimally been adjusted using the GWOJOS algorithm formed by combining the Grey Wolf Optimization (GWO) algorithm and the recently defined the Joint Opposite Selection (JOS) feature. The JOS brings a mutual reinforcement by a joint of the two opposition strategies Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. During the optimization phase, JOS helps GWO attack the target quickly by employing SLO. DO help GWO find more opportunities to find the most suitable prey. The GWO is able to improve its performance with JOS. This combination helps accelerating the convergence rate of GWO. We assessed GWOJOS's performance using the benchmark functions from the IEEE Congress on Evolutionary Computation 2017 (CEC2017). The benchmark covers composition, hybrid, multimodal, and unimodal functions. The NIOPID-based speed control system for DC-motor using the GWOJOS algorithm has been designed using a time domain objective function that takes into account the performance criteria (maximum overshoot, steady-state error, rising time, and settling time). Some analyses, including robustness, time and frequency domain simulations, have been used to evaluate the performance of the proposed novel approach. The evaluation results have shown that the performance of GWOJOS was better than the performance of GWO, Slime Mould Algorithm (SMA), Atom Search Optimization (ASO), Simulated Annealing (SA) and the hybrid optimization algorithm created by opposition-based learning (OBL) strategy of SA and SMA algorithms (OBLSMASA).

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Bu Makaleye Atıf Yap

Doğan, K., Başak, H. (2025). Geliştirilmiş Gri Kurt Optimizasyon Algoritmasına Dayalı Kesirli Mertebeden Oransal İntegral Türevsel PID Denetleyici Tasarımı. Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 6 Sayı: 1, 220–244. https://doi.org/10.53501/rteufemud.1545913

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