Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 8 Sayı: 2, 652 - 663, 28.06.2019
https://doi.org/10.17798/bitlisfen.496782

Öz

Kaynakça

  • [1] Ahmed H., Rajoriya A. 2017. A Hybrid of Sliding Mode Control and Fuzzy Logic Control Using a Fuzzy Supervisory Switched System for DC Motor Speed Control. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (3): 1993-2004.
  • [2] Hekimoğlu B., Ekinci S. 2018. Grasshopper Optimization Algorithm for Automatic Voltage Regulator System. 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp. 152-156, May 3-5, Istanbul, Turkey.
  • [3] Ekinci S., Demiroren A. 2015. PSO based PSS Design for Transient Stability Enhancement. IUJournal of Electrical & Electronics Engineering, 15 (1): 1855-1862.
  • [4] Ekinci S., Hekimoğlu B. 2017. Multi-machine Power System Stabilizer Design via HPA Algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University, 32 (4): 1271-1285.
  • [5] El-Deen A.T., Mahmoud A.A.H., El-Sawi A.R. 2015. Optimal PID Tuning for DC Motor Speed Controller based on Genetic Algorithm. International Review of Automatic Control, 8 (1): 80-85.
  • [6] Mishra A.K., Tiwari V. K., Kumar R. Verma T. 2013. Speed Control of DC Motor Using Artificial Bee Colony Optimization Technique. International Conference on Control, Automation, Robotics and Embedded Systems (CARE), pp. 1-6, Dec 16-18, Jabalpur, India.
  • [7] Achanta R.K., Pamula, V.K. 2017. DC Motor Speed Control Using PID controller Tuned by Jaya Optimization Algorithm. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 983-987, Sep 21-22, Chennai, India.
  • [8] Khalilpuor M., Razmjooy N., Hosseini H., Moallem P. 2011. Optimal Control of DC Motor Using Invasive Weed Optimization (IWO) Algorithm, Majlesi Conference on Electrical Engineering, Aug, Majlesi New Town, Isfahan, Iran.
  • [9] Khanam I., Parmar G. 2017. Application of SFS Algorithm in Control of DC Motor and Comparative Analysis. 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), pp. 256-261, Oct 26-28, Mathura, India.
  • [10] Agarwal J., Parmar G., Gupta R., Sikander A. 2018. Analysis of Grey Wolf Optimizer based Fractional Order PID Controller in Speed Control of DC Motor. Microsystem Technologies, 24 (12): 4997-5006. B. Hekimoğlu / BEÜ Fen Bilimleri Dergisi 8 (2), 652-663, 2019 663
  • [11] Jaddi N.S., Alvankarian J., Abdullah S. 2017. Kidney-inspired Algorithm for Optimization Problems. Communications in Nonlinear Science and Numerical Simulation, 42: 358-369.
  • [12] Jaddi N.S., Abdullah S. 2018. Optimization of Neural Network Using Kidney-inspired Algorithm with Control of Filtration Rate and Chaotic Map for Real-world Rainfall Forecasting. Engineering Applications of Artificial Intelligence, 67: 246-259.
  • [13] Ekinci S., Hekimoğlu B., Uysal E. 2019. Kidney-inspired Algorithm for Determination of PID Power System Stabilizer Parameters. Journal of Polytechnic, 22 (2): 453-460.
  • [14] Ekinci S., Demiroren A., Hekimoglu B. 2019. Parameter Optimization of Power System Stabilizers via Kidney-inspired Algorithm. Transactions of the Institute of Measurement and Control, 41 (5): 1405-1417.
  • [15] Liang Y., Niu D., Wang H., Chen H. 2017. Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China. Energies, 10 (3): 391. https://doi.org/10.3390/en10030391.
  • [16] Ehteram M., Karami H., Mousavi S.F., Farzin S., Celeste A.B., Shafie A.E. 2018. Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm. Water Resources Management, 32 (14): 4681-4706.
  • [17] Ekinci S., Hekimoglu B. 2019. Improved Kidney-inspired Algorithm Approach for Tuning of PID Controller in AVR System. IEEE Access, 7: 39935-39947.
  • [18] Ogata K. 2004. System Dynamics. 4 th Ed., Pearson Prentice Hall, Upper Saddle River, NJ.
  • [19] Ogata K. 2002. Modern Control Engineering. 4 th Ed., Prentice Hall Inc., Upper Saddle River, NJ.
  • [20] Ekinci S., Hekimoglu B., Kaya S. 2018. Tuning of PID Controller for AVR System Using Salp Swarm Algorithm. International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 424-429. Sep 28-30, Malatya, Turkey.
  • [21] Gaing Z.L. 2004. A particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR system. IEEE Transactions on Energy Conversion, 19 (2): 384-391.
  • [22] Hekimoglu B. 2019. Sine-cosine Algorithm Based Optimization for Automatic Voltage Regulator System. Transactions of the Institute of Measurement and Control, 41 (6): 1761-1771.
  • [23] Mirjalili S., Mirjalili, S.M., Lewis, A. 2014. Grey wolf optimizer. Advances in Engineering Software, 69: 46-61.
  • [24] Mehrabian A.R., Lucas, C. 2006. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1 (4): 355-366.
  • [25] Salimi H. 2015. Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-Based Systems, 75: 1-18.

Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü

Yıl 2019, Cilt: 8 Sayı: 2, 652 - 663, 28.06.2019
https://doi.org/10.17798/bitlisfen.496782

Öz

DC motor hız kontrol sistemlerinin birçok endüstriyel uygulamasında, çoğunlukla oransal-integral-türevsel (PID)
kontrolörler kullanılmaktadır. Bu çalışmada, DC motor hız kontrolünün en uygun PID kontrolör parametreleri,
yani oransal (Kp), integral (Ki) ve türev (Kd) kazançları, etkin ve hızlı bir ayar yöntemi olan böbrek-ilhamlı
algoritma (Kidney-inspired Algorithm - KA) ile belirlenmektedir. Kontrol sisteminin tasarımında, kontrolör
parametrelerinin KA tarafından optimize edilebilmesi için zaman bölgesi tabanlı bir performans ölçütü
kullanılmıştır. Bu amaç fonksiyonu ile önerilen yaklaşımın performansını değerlendirmek için son yıllarda
yayımlanmış gri kurt optimizasyon (Grey Wolf Optimization - GWO) algoritması, istilacı ot optimizasyon
(Invasive Weed Optimization – IWO) algoritması ve stokastik fraktal arama (Stochastic Fractal Search – SFS)
algoritması gibi diğer modern sezgisel-üstü optimizasyon algoritmalarına dayalı yaklaşımlarla karşılaştırmalar
yapılmıştır. Simülasyon sonuçlarından, DC motorun hız kontrolü için tasarlanan KA tabanlı PID (KA-PID)
kontrolörün kapalı çevrim sisteminin aşım, yerleşme zamanı ve yükselme zamanı gibi sistem karakteristiklerini en
az iterasyonla önemli ölçüde iyileştirdiği görülmüştür. KA-PID kontrolör yaklaşımının gürbüzlük analizi de, DC
motor parametrelerindeki değişikliklerle gerçekleştirilmiştir.

Kaynakça

  • [1] Ahmed H., Rajoriya A. 2017. A Hybrid of Sliding Mode Control and Fuzzy Logic Control Using a Fuzzy Supervisory Switched System for DC Motor Speed Control. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (3): 1993-2004.
  • [2] Hekimoğlu B., Ekinci S. 2018. Grasshopper Optimization Algorithm for Automatic Voltage Regulator System. 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp. 152-156, May 3-5, Istanbul, Turkey.
  • [3] Ekinci S., Demiroren A. 2015. PSO based PSS Design for Transient Stability Enhancement. IUJournal of Electrical & Electronics Engineering, 15 (1): 1855-1862.
  • [4] Ekinci S., Hekimoğlu B. 2017. Multi-machine Power System Stabilizer Design via HPA Algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University, 32 (4): 1271-1285.
  • [5] El-Deen A.T., Mahmoud A.A.H., El-Sawi A.R. 2015. Optimal PID Tuning for DC Motor Speed Controller based on Genetic Algorithm. International Review of Automatic Control, 8 (1): 80-85.
  • [6] Mishra A.K., Tiwari V. K., Kumar R. Verma T. 2013. Speed Control of DC Motor Using Artificial Bee Colony Optimization Technique. International Conference on Control, Automation, Robotics and Embedded Systems (CARE), pp. 1-6, Dec 16-18, Jabalpur, India.
  • [7] Achanta R.K., Pamula, V.K. 2017. DC Motor Speed Control Using PID controller Tuned by Jaya Optimization Algorithm. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 983-987, Sep 21-22, Chennai, India.
  • [8] Khalilpuor M., Razmjooy N., Hosseini H., Moallem P. 2011. Optimal Control of DC Motor Using Invasive Weed Optimization (IWO) Algorithm, Majlesi Conference on Electrical Engineering, Aug, Majlesi New Town, Isfahan, Iran.
  • [9] Khanam I., Parmar G. 2017. Application of SFS Algorithm in Control of DC Motor and Comparative Analysis. 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), pp. 256-261, Oct 26-28, Mathura, India.
  • [10] Agarwal J., Parmar G., Gupta R., Sikander A. 2018. Analysis of Grey Wolf Optimizer based Fractional Order PID Controller in Speed Control of DC Motor. Microsystem Technologies, 24 (12): 4997-5006. B. Hekimoğlu / BEÜ Fen Bilimleri Dergisi 8 (2), 652-663, 2019 663
  • [11] Jaddi N.S., Alvankarian J., Abdullah S. 2017. Kidney-inspired Algorithm for Optimization Problems. Communications in Nonlinear Science and Numerical Simulation, 42: 358-369.
  • [12] Jaddi N.S., Abdullah S. 2018. Optimization of Neural Network Using Kidney-inspired Algorithm with Control of Filtration Rate and Chaotic Map for Real-world Rainfall Forecasting. Engineering Applications of Artificial Intelligence, 67: 246-259.
  • [13] Ekinci S., Hekimoğlu B., Uysal E. 2019. Kidney-inspired Algorithm for Determination of PID Power System Stabilizer Parameters. Journal of Polytechnic, 22 (2): 453-460.
  • [14] Ekinci S., Demiroren A., Hekimoglu B. 2019. Parameter Optimization of Power System Stabilizers via Kidney-inspired Algorithm. Transactions of the Institute of Measurement and Control, 41 (5): 1405-1417.
  • [15] Liang Y., Niu D., Wang H., Chen H. 2017. Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China. Energies, 10 (3): 391. https://doi.org/10.3390/en10030391.
  • [16] Ehteram M., Karami H., Mousavi S.F., Farzin S., Celeste A.B., Shafie A.E. 2018. Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm. Water Resources Management, 32 (14): 4681-4706.
  • [17] Ekinci S., Hekimoglu B. 2019. Improved Kidney-inspired Algorithm Approach for Tuning of PID Controller in AVR System. IEEE Access, 7: 39935-39947.
  • [18] Ogata K. 2004. System Dynamics. 4 th Ed., Pearson Prentice Hall, Upper Saddle River, NJ.
  • [19] Ogata K. 2002. Modern Control Engineering. 4 th Ed., Prentice Hall Inc., Upper Saddle River, NJ.
  • [20] Ekinci S., Hekimoglu B., Kaya S. 2018. Tuning of PID Controller for AVR System Using Salp Swarm Algorithm. International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 424-429. Sep 28-30, Malatya, Turkey.
  • [21] Gaing Z.L. 2004. A particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR system. IEEE Transactions on Energy Conversion, 19 (2): 384-391.
  • [22] Hekimoglu B. 2019. Sine-cosine Algorithm Based Optimization for Automatic Voltage Regulator System. Transactions of the Institute of Measurement and Control, 41 (6): 1761-1771.
  • [23] Mirjalili S., Mirjalili, S.M., Lewis, A. 2014. Grey wolf optimizer. Advances in Engineering Software, 69: 46-61.
  • [24] Mehrabian A.R., Lucas, C. 2006. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1 (4): 355-366.
  • [25] Salimi H. 2015. Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-Based Systems, 75: 1-18.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Baran Hekimoğlu 0000-0002-1839-025X

Yayımlanma Tarihi 28 Haziran 2019
Gönderilme Tarihi 13 Aralık 2018
Kabul Tarihi 17 Mayıs 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 8 Sayı: 2

Kaynak Göster

IEEE B. Hekimoğlu, “Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 8, sy. 2, ss. 652–663, 2019, doi: 10.17798/bitlisfen.496782.



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