Araştırma Makalesi
Farklı Yük Çeşitleri İçin Parçacık Sürü Optimizasyonu ve Ziegler-Nichols Metodunun DC Motor Hız Kontrolü Probleminde Karşılaştırılması
Öz
Bu çalışmada DC motor hız kontrolü problemini Ziegler-Nichols (ZN) ve Parçacık Sürü Optimizasyonu (PSO) yaklaşımları ile çözerek performans karşılaştırılması yapılmıştır. Farklı referans çeşitleri ve farklı yük çeşitleri ile sistem sürülerek çıkış gözlemlenmiştir. Mutlak hataların toplamı cinsinden bir maliyet hesabı ve maliyet hesabının tersi olarak performans hesabı yapılmıştır. Bütün referans ve yüklerde PSO yaklaşımının performansı ZN yaklaşımının performansından fazla olduğu gözlemlenmiştir. Özellikle referans ve yük karmaşıklaştıkça bu performans farkının arttığı sonucuna varılıp sonuçlar hem şekil hem de tablo olarak verilmiştir.
Anahtar Kelimeler
Kaynakça
- Optimal Control of DC motor using Invasive Weed Optimization (IWO) Algorithm. (2011). Majlesi Conference on Electrical Engineering.
- A. A. M. Zahir, S. S. N. Alhady, W. A. F. W. Othman and M. F. Ahmad. (2018). Genetic Algorithm Optimization of PID Controller for Brushed DC Motor. Intelligent Manufacturing & Mechatronics. Lecture Notes in Mechanical Engineering. Springer, Singapore, 427-437.
- B Gökçe; YB Koca; Y Aslan; CO Gökçe. (2021). PARTICLE SWARM OPTIMIZATION-BASED OPTIMAL PID CONTROL OF AN AGRICULTURAL MOBILE ROBOT. Comptes rendus de l' Acad'emie bulgare des Sciences, 568-575.
- 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 , 67-77.
- Gökbulut, P. D. (2019, Ocak). Kontrol Sistemlerinin Analiz ve Tasarımı. 10 25, 2021 tarihinde eem.tf.firat.edu.tr: http://eem.tf.firat.edu.tr/subdomain_files/eem.tf.firat.edu.tr/files/36/Kontrol%20sistemleri%20ders%20notu.pdf adresinden alındı
- 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.
- Harun Yazgan, Furkan Yener, Semih Soysal, Ahmet Gür. (2019). Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor. Sakarya University Journal of Science, 162-174. doi:10.16984/saufenbilder.376464
- Noorulden Basil Mohamadwasel, Oguz Bayat. (2019). Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors. International Journal of Computer Science and Mobile Computing, 3(8), 152-160.
- PID Kontrol Parametrelerinin Ayarlanması. (2018, 03 9). https://acikders.ankara.edu.tr/: https://acikders.ankara.edu.tr/pluginfile.php/70147/mod_resource/content/0/GDM404_11.pdf adresinden alındı
- Siri Weerasooriya M. A. El-Sharkawi. (1991). IDENTIFICATION AND CONTROL OF A DC MOTOR USING BACK-PROPAGATION NEURAL NETWORKS. IEEE Transactions on Energy Conversion, 6(4), 663-669.
- Tri Kuntoro Priyambodo, Agfianto Eko Putra, Andi Dharmawan. (2015). Optimizing control based on ant colony logic for Quadrotor stabilization. 2015 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) (s. 1-4). IEEE.
- Zhi Qi, Qian Shi, and Hui Zhang. (2019). Tuning of digital PID controllers using particle swarm optimization algorithm for a CAN-based DC motor subject to stochastic delays. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. doi:DOI 10.1109/TIE.2019.2934030
Comparison of Particle Swarm Optimization and Ziegler-Nichols Methods in the Problem of DC Motor Speed Control Under Different Loads
Öz
In this study, DC motor velocity control problem is solved using two different approaches; namely Ziegler-Nichols (ZN) and Particle Swarm Optimization (PSO). The performances of two approaches are measured with different types of references and disturbances. Performance is measured using sum of absolute errors. It is observed that PSO approach shows better performance in all configurations. An increased performance difference is observed especially in complex references and complex disturbances. Results are given both in figures and as a table.
Anahtar Kelimeler
Kaynakça
- Optimal Control of DC motor using Invasive Weed Optimization (IWO) Algorithm. (2011). Majlesi Conference on Electrical Engineering.
- A. A. M. Zahir, S. S. N. Alhady, W. A. F. W. Othman and M. F. Ahmad. (2018). Genetic Algorithm Optimization of PID Controller for Brushed DC Motor. Intelligent Manufacturing & Mechatronics. Lecture Notes in Mechanical Engineering. Springer, Singapore, 427-437.
- B Gökçe; YB Koca; Y Aslan; CO Gökçe. (2021). PARTICLE SWARM OPTIMIZATION-BASED OPTIMAL PID CONTROL OF AN AGRICULTURAL MOBILE ROBOT. Comptes rendus de l' Acad'emie bulgare des Sciences, 568-575.
- 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 , 67-77.
- Gökbulut, P. D. (2019, Ocak). Kontrol Sistemlerinin Analiz ve Tasarımı. 10 25, 2021 tarihinde eem.tf.firat.edu.tr: http://eem.tf.firat.edu.tr/subdomain_files/eem.tf.firat.edu.tr/files/36/Kontrol%20sistemleri%20ders%20notu.pdf adresinden alındı
- 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.
- Harun Yazgan, Furkan Yener, Semih Soysal, Ahmet Gür. (2019). Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor. Sakarya University Journal of Science, 162-174. doi:10.16984/saufenbilder.376464
- Noorulden Basil Mohamadwasel, Oguz Bayat. (2019). Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors. International Journal of Computer Science and Mobile Computing, 3(8), 152-160.
- PID Kontrol Parametrelerinin Ayarlanması. (2018, 03 9). https://acikders.ankara.edu.tr/: https://acikders.ankara.edu.tr/pluginfile.php/70147/mod_resource/content/0/GDM404_11.pdf adresinden alındı
- Siri Weerasooriya M. A. El-Sharkawi. (1991). IDENTIFICATION AND CONTROL OF A DC MOTOR USING BACK-PROPAGATION NEURAL NETWORKS. IEEE Transactions on Energy Conversion, 6(4), 663-669.
- Tri Kuntoro Priyambodo, Agfianto Eko Putra, Andi Dharmawan. (2015). Optimizing control based on ant colony logic for Quadrotor stabilization. 2015 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) (s. 1-4). IEEE.
- Zhi Qi, Qian Shi, and Hui Zhang. (2019). Tuning of digital PID controllers using particle swarm optimization algorithm for a CAN-based DC motor subject to stochastic delays. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. doi:DOI 10.1109/TIE.2019.2934030
Toplam 12 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 30 Ocak 2022 |
| Yayımlanma Tarihi | 31 Ocak 2022 |
| Yayımlandığı Sayı | Yıl 2022 Sayı: 33 |
- Makale Dosyaları
