FOPID TABANLI ELEKTRİKLİ ARAÇ HIZ KONTROLÜNÜN SEZGİSEL OPTİMİZASYON ALGORİTMALARIYLA KARŞILAŞTIRMALI ANALİZİ

Year 2025, Volume: 14 Issue: 4, 209 - 221, 30.12.2025

Gülten Yılmaz

https://doi.org/10.46810/tdfd.1791881

Abstract

Bu çalışmada, bir elektrikli aracın hız kontrolü için kullanılan fraksiyonel mertebeli PID (FOPID) kontrolörün parametreleri, beş farklı sezgisel optimizasyon algoritması ile optimize edilmiştir: Genetik Algoritma (GA), Gri Kurt Optimizasyonu (GWO), Harris Hawk Optimizasyonu (HHO), Parçacık Sürü Optimizasyonu (PSO) ve Salp Sürüsü Algoritması (SSA). Kontrol sistemi, MATLAB/Simulink ortamında modellenmiş ve kapalı döngüde hız kontrol performansı test edilmiştir. Optimizasyon sürecinde, performans kriterleri olarak yüzde aşım (%OS), yerleşme süresi (settling time), yükselme süresi (rise time) ve ortalama kare hatası (MSE) kullanılmıştır. Her algoritma ile elde edilen sonuçlar, belirtilen performans kriterleri açısından karşılaştırmalı olarak değerlendirilmiştir. Sonuçlar, farklı algoritmaların FOPID parametre optimizasyonundaki başarımlarının uygulamaya ve performans kriterlerine göre değişkenlik gösterdiğini ortaya koymuştur. Elde edilen bulgular, elektrikli araçlarda hız kontrol performansını artırmaya yönelik algoritma seçiminde önemli bir referans sağlamaktadır.

Keywords

Elektrikli araç , KDPID , Hız kontrolü , GA , GWO , HHO , PSO

Comparative Analysis of FOPID-Based Electric Vehicle Speed Control Using Heuristic Optimization Algorithms

Abstract

In this study, the parameters of a Fractional Order PID (FOPID) controller used for the speed control of an electric vehicle (EV) were optimized using five different heuristic optimization algorithms: Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA). The control system was modeled in the MATLAB/Simulink environment, and the speed control performance was tested in a closed-loop configuration. In the optimization process, performance criteria such as percentage overshoot (%OS), settling time (t_s), rise time 〖(t〗_r), and mean squared error (MSE) were used. The results obtained with each algorithm were evaluated comparatively in terms of the specified performance criteria. The results revealed that the performance of different algorithms in FOPID parameter optimization varies depending on the application and performance criteria. The findings provide an important reference for the selection of appropriate algorithms to enhance speed control performance in electric vehicles.

Keywords

Electric vehicle , FOPID , Speed control , GA , GWO , HHO , PSO

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