Dergiler / International Journal of Computational and Experimental Science and Engineering / 2026 / Cilt: 9 Sayı: 1

Disturbance Rejection Performance Comparison of PSO and ZN Methods for Various Disturbance Frequencies

Celal Onur Gökçe(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0002-5247-7850
Volkan Durusu(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0001-7726-5435
Ridvan Unal(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0001-6842-7471
Sayfa
17–19
Yayın Tarihi
2023-03-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Comments are done on results and future study is planned.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this study Proportional-Integral-Derivative (PID) control of brushed DC Motor is analyzed.
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Temel Bulgular & Veri:The system is tested under sinusoidal disturbance of varying frequencies in order to evaluate and compare disturbance rejection performances.
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Sonuç & Katkı:Comments are done on results and future study is planned.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:PID control · Ziegler-Nichols · Particle Swarm Optimization

Abstract

In this study Proportional-Integral-Derivative (PID) control of brushed DC Motor is analyzed. The parameters of the PID controller are tuned with two different approaches, namely Ziegler-Nichols (ZN) and Particle Swarm Optimization (PSO). The system is tested under sinusoidal disturbance of varying frequencies in order to evaluate and compare disturbance rejection performances. It is shown that PSO approach has clearly higher performance compared with ZN approach for all disturbance frequencies. Simulations are done using Python programming language with trapezoid rule for differentiation and integration. Results are given in both figures and tables. Comments are done on results and future study is planned.

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Bu Makaleye Atıf Yap

Gökçe, C. O., Durusu, V., Unal, R. (2023). Disturbance Rejection Performance Comparison of PSO and ZN Methods for Various Disturbance Frequencies. International Journal of Computational and Experimental Science and Engineering, Cilt: 9 Sayı: 1, 17–19. https://doi.org/10.22399/ijcesen.1202255

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Kaynakça (7)

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  2. R. A. Krohling and J. P. Rey, (2001). Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Transactions on Evolutionary Computation, 5(1);78-82. doi: 10.1109/4235.910467.
  3. H.E.A.Ibrahima, F.N.Hassan, Anas O.Shomer. (2014) Optimal PID control of a brushless DC motor using PSO and BF techniques. Ain Shams Engineering Journal, 2(5);391-398.
  4. Baoye Song, Yihui Xiao and Lin Xu (2020). Design of fuzzy PI controller for brushless DC motor based on PSO–GSA algorithm, Systems Science & Control Engineering, 8(1);67-77. DOI: 10.1080/21642583.2020.1723144.
  5. Yazgan H, Yener F, Soysal S, Gür A, (2019). Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor, Sakarya University Journal of Science, 23(2); 162-174.
  6. Ziegler J G, Nichols N B, 1942, Optimum Settings for Automatic Controllers, Transactions of the American Society of Mechanical Engineers, 64(11);759-765.
  7. Akyol S, Alataş B, (2012). Current Swarm Intelligence Optimization Algorithms. Nevşehir University Journal of Graduate School of Natural and Applied Sciences, 1(1);36-50.