Öz
Bu makale PI kontrolör katsayılarının (Oransal kazanç Kp and Integral kazanç Ki) optimum değerlerini bulmak için Elman Yapay Sinir Ağları (EYSA) modeli kullanılan Pareto tabanlı çok amaçlı optimizasyonu ve Yanıt Yüzey Yöntemi’nin (YYY) karşılaştırılmasını sunmaktadır. Amaç fonksiyonu olarak oturma süresi (Ts) ve maksimum aşma (Mo) seçilmiştir. Çalışmanın amacı, PI kontrolör parametre katsayıları olan Kp ve Ki’nin optimum değerlerinin bulunmasıdır. İlk olarak, Minitab programı kullanılarak deney tasarımı gerçekleştirilmiştir. Daha sonra fırçasız doğru akım motoru hız kontrol sisteminin matematiksel modelinin oluşturulması için YYY ve EYSA modeli ayrı ayrı elde edilmiştir. Son olarak da, optimizasyon işlemi her iki yöntem ile ayrı ayrı gerçekleştirilmiştir. Optimizasyon işlemleri sonucunda, katsayıların optimum değerleri ve Pareto eğrisi elde edilmiştir. Elde edilen optimum değerler, sistemde kontrolör içerisine yazılarak fırçasız DA motorun gerçek zamanlı çıkışları elde edilmiştir. Sonuçlar karşılaştırıldığında, Pareto tabanlı EYSA yönteminde daha iyi performans sağlandığı görülmüştür.
Anahtar Kelimeler
Fırçasız DA motor YYY EYSA Pareto eğrisi çok amaçlı optimizasyon
Kaynakça
- Demirtas, M., Off-line tuning of a PI speed controller for a permanent magnet brushless DC motor using DSP, Energy Conversion Management, 52, 1, 264–273, (2011).
- Tajjudin, M., Rahiman, M.H.F., Arshad N. M. ve Adnan, R., Robust fractional-order PI controller with Ziegler-Nichols rules, International Journal of Electrical and Computer Engineering, 7, 7, 1036-1041, (2013).
- Çoban, R. ve Erçin, Ö., Multi-objective Bees Algorithm to Optimal Tuning of PID Controller, Cukurova University Journal of the Faculty of Engineering and Architecture, 27, 2, 13–26, (2012).
- Erkol, H.O., Ters sarkaç sisteminin yapay arı kolonisi algoritması ile optimizasyonu, Politeknik Dergisi, 20, 4, 863–868, (2017).
- Ercan, K., Mühürcü, A., Mühürcü, G., ve Aydoğan, E., Buck Dönüştürücü Çıkış Geriliminin Karınca Koloni Algoritması Kullanılarak PI Tabanlı Optimal Kontrolü, Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32, 4, 153–162, (2017).
- Altun, M., Celik, Y., ve Güneş, M., Doğru Akım Motorunun Hız Denetiminde Parçacık Sürü Optimizasyonu Tabanlı PID, Klasik PID ve Bulanık Tipi Denetim Yöntemlerinin Başarımlarının İncelenmesi, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20, 4, 158–167, (2017).
- Ilten, E. ve Demirtas, M., Off-Line Tuning of Fractional Order PIλ Controller by Using Response Surface Method for Induction Motor Speed Control, Journal of Control Engineering and Applied Informatics, 18, 2, 20–27, (2016).
- Demirtas, M. ve Musa, A., A comparative study of neural networks and fuzzy systems in modeling of a nonlinear dynamic system, An International Journal of Optimization and Control: Theories & Applications, 1, 1, 65–73, (2011).
- Ustun, S.V. ve Demirtas, M., Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms, Electrical Engineering, 87 , 2, 77–82, (2005).
- Demirtas, M., ve Karaoglan, A.D., Optimization of PI parameters for DSP-based permanent magnet brushless motor drive using response surface methodology, Energy Conversion and Management, 56, 104–111, (2012).
- Dutta, J.R., Dutta, P.K. ve Banerjee, R., Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models, Process Biochemistry, 39, 12, 2193–2198, (2004).
- Desai, K.M., Survase, S.A., Saudagar, P.S., Lele, S.S. ve Singhal, R.S., Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan, Biochemical Engineering Journal, 41, 3, 266–273, (2008).
- Gomes, H.M. ve Awruch, A.M., Comparison of response surface and neural network with other methods for structural reliability analysis, Structural safety/., 26 , 1, 49–67, (2004).
- Bas, D. ve Boyaci, I.H., Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, Journal of Food Engineering, 78, 3, 846–854, (2007).
- Okay, F.Y. ve Özdemir, S., Kablosuz algılayıcı ağlarda kapsama alanının çok amaçlı evrimsel algoritmalar ile artırılması, Journal of the Faculty of Engineering and Architecture of Gazi University, 30, 2, (2015).
- Tezer, T., Yaman, R. ve Yaman, G., Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems, Renewable and Sustainable Energy Reviews, 73, 840–853, (2017).
- Sharafi, M. ve ELMekkawy, T.Y., Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach, Renewable Energy, 68, 67–79, (2014).
- Subasi, A., Sahin, B. ve Kaymaz, I., Multi-objective optimization of a honeycomb heat sink using Response Surface Method, International Journal of Heat and Mass Transfer, 101, 295–302, (2016).
- Wei, L. ve Yuying, Y., Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm, Journal of Materials Processing Technology, vol. 208, 1, 499–506, (2008).
- Madavan, N.K., Multiobjective optimization using a Pareto differential evolution approach, Proceedings, Evolutionary Computation, 2, 1145–1150, Honolulu-USA, (2002).
- Turan, M.D. ve Altundoğan, H. S., Hidrometalurjik Araştırmalarda Yanıt Yüzey Yöntemlerinin (YYY) Kullanımı, Bilimsel Madencilik Dergisi, 50, 3, 11–23, (2011).
- Dutka, M., Ditaranto, M. ve Løvås, T., Application of a central composite design for the study of NOx emission performance of a low NOx burner, Energies, 8, 5, 3606–3627, (2015).
- Karakuş, M.O. ve Altın, C., İleri Beslemeli Ağ ile Elman Ağı Kullanılarak Hidroelektrik Santralinin Verimi Hesabı: Hirfanlı Barajı Uygulama Örneği, Electronic Letters on Science&Engineering, 10, 2, 1-14, (2014).
- Calgan, H., Demirtas, M. ve Balci, M.E., Capacitive Power and Torque Estimation for Self-Excited Induction Generator with Elman Neural Network, Proceedings, 3th Internetional Conference on Engineering and Natural Science, 878–883, Budapest-Hungary, (2017).
- Sharkawy, A.B., Genetic fuzzy self-tuning PID controllers for antilock braking systems, Engineering Applications of Artificial Intelligence, 23, 7, 1041–1052, (2010).
- Chen, X., Pareto tree searching genetic algorithm: Approaching Pareto optimal front by searching Pareto optimal tree, Tech. Report Nk-CS-200, 1–2, (2001).
- Moura, P. S. ve de Almeida, A. T., Multi-objective optimization of a mixed renewable system with demand-side management, Renewable & Sustainable Energy Reviews, 14, 5, 1461–1468, (2010).
- Demirtas, M., Ilten, E., Calgan, H., Pareto-Based Multi-objective Optimization for Fractional Order PIλ Speed Control of Induction Motor by Using Elman Neural Network, Arabian Journal for Science and Engineering, https://doi.org/10.1007/s13369-018-3364-2.
Pareto-based multi-objective optimization of PI coefficients for speed control of brushless DC motor
Öz
This paper present comparison for finding optimum values of PI controller coefficients (Proportional gain Kp and Integral gain Ki) by using Elman Neural Network (ENN) model with Pareto based multi-objective optimization method and Response Surface Method (RSM). Objective functions are chosen as settling time (Ts) and maximum overshoot (Mo). The aim of the study is to optimize tuning parameters of PI controller Kp and Ki. Firstly, experimental design has been carried out by using Minitab program. Then, RSM and ENN model have been obtained separately to construct the mathematical model of the brushless DC (BLDC) motor speed control system. Finally, optimization process has been carried out with both methods. Optimum values of coefficients and Pareto front have been obtained after optimization process. The real time outputs of the BLDC motor are obtained by using the obtained optimal values of the coefficients inside of the controller in the system. When compared the results, the better performance is provided by the Pareto based-ENN method.
Anahtar Kelimeler
Brushless DC motor RSM ENN Pareto front multi-objective optimization
Kaynakça
- Demirtas, M., Off-line tuning of a PI speed controller for a permanent magnet brushless DC motor using DSP, Energy Conversion Management, 52, 1, 264–273, (2011).
- Tajjudin, M., Rahiman, M.H.F., Arshad N. M. ve Adnan, R., Robust fractional-order PI controller with Ziegler-Nichols rules, International Journal of Electrical and Computer Engineering, 7, 7, 1036-1041, (2013).
- Çoban, R. ve Erçin, Ö., Multi-objective Bees Algorithm to Optimal Tuning of PID Controller, Cukurova University Journal of the Faculty of Engineering and Architecture, 27, 2, 13–26, (2012).
- Erkol, H.O., Ters sarkaç sisteminin yapay arı kolonisi algoritması ile optimizasyonu, Politeknik Dergisi, 20, 4, 863–868, (2017).
- Ercan, K., Mühürcü, A., Mühürcü, G., ve Aydoğan, E., Buck Dönüştürücü Çıkış Geriliminin Karınca Koloni Algoritması Kullanılarak PI Tabanlı Optimal Kontrolü, Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32, 4, 153–162, (2017).
- Altun, M., Celik, Y., ve Güneş, M., Doğru Akım Motorunun Hız Denetiminde Parçacık Sürü Optimizasyonu Tabanlı PID, Klasik PID ve Bulanık Tipi Denetim Yöntemlerinin Başarımlarının İncelenmesi, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20, 4, 158–167, (2017).
- Ilten, E. ve Demirtas, M., Off-Line Tuning of Fractional Order PIλ Controller by Using Response Surface Method for Induction Motor Speed Control, Journal of Control Engineering and Applied Informatics, 18, 2, 20–27, (2016).
- Demirtas, M. ve Musa, A., A comparative study of neural networks and fuzzy systems in modeling of a nonlinear dynamic system, An International Journal of Optimization and Control: Theories & Applications, 1, 1, 65–73, (2011).
- Ustun, S.V. ve Demirtas, M., Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms, Electrical Engineering, 87 , 2, 77–82, (2005).
- Demirtas, M., ve Karaoglan, A.D., Optimization of PI parameters for DSP-based permanent magnet brushless motor drive using response surface methodology, Energy Conversion and Management, 56, 104–111, (2012).
- Dutta, J.R., Dutta, P.K. ve Banerjee, R., Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models, Process Biochemistry, 39, 12, 2193–2198, (2004).
- Desai, K.M., Survase, S.A., Saudagar, P.S., Lele, S.S. ve Singhal, R.S., Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan, Biochemical Engineering Journal, 41, 3, 266–273, (2008).
- Gomes, H.M. ve Awruch, A.M., Comparison of response surface and neural network with other methods for structural reliability analysis, Structural safety/., 26 , 1, 49–67, (2004).
- Bas, D. ve Boyaci, I.H., Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, Journal of Food Engineering, 78, 3, 846–854, (2007).
- Okay, F.Y. ve Özdemir, S., Kablosuz algılayıcı ağlarda kapsama alanının çok amaçlı evrimsel algoritmalar ile artırılması, Journal of the Faculty of Engineering and Architecture of Gazi University, 30, 2, (2015).
- Tezer, T., Yaman, R. ve Yaman, G., Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems, Renewable and Sustainable Energy Reviews, 73, 840–853, (2017).
- Sharafi, M. ve ELMekkawy, T.Y., Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach, Renewable Energy, 68, 67–79, (2014).
- Subasi, A., Sahin, B. ve Kaymaz, I., Multi-objective optimization of a honeycomb heat sink using Response Surface Method, International Journal of Heat and Mass Transfer, 101, 295–302, (2016).
- Wei, L. ve Yuying, Y., Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm, Journal of Materials Processing Technology, vol. 208, 1, 499–506, (2008).
- Madavan, N.K., Multiobjective optimization using a Pareto differential evolution approach, Proceedings, Evolutionary Computation, 2, 1145–1150, Honolulu-USA, (2002).
- Turan, M.D. ve Altundoğan, H. S., Hidrometalurjik Araştırmalarda Yanıt Yüzey Yöntemlerinin (YYY) Kullanımı, Bilimsel Madencilik Dergisi, 50, 3, 11–23, (2011).
- Dutka, M., Ditaranto, M. ve Løvås, T., Application of a central composite design for the study of NOx emission performance of a low NOx burner, Energies, 8, 5, 3606–3627, (2015).
- Karakuş, M.O. ve Altın, C., İleri Beslemeli Ağ ile Elman Ağı Kullanılarak Hidroelektrik Santralinin Verimi Hesabı: Hirfanlı Barajı Uygulama Örneği, Electronic Letters on Science&Engineering, 10, 2, 1-14, (2014).
- Calgan, H., Demirtas, M. ve Balci, M.E., Capacitive Power and Torque Estimation for Self-Excited Induction Generator with Elman Neural Network, Proceedings, 3th Internetional Conference on Engineering and Natural Science, 878–883, Budapest-Hungary, (2017).
- Sharkawy, A.B., Genetic fuzzy self-tuning PID controllers for antilock braking systems, Engineering Applications of Artificial Intelligence, 23, 7, 1041–1052, (2010).
- Chen, X., Pareto tree searching genetic algorithm: Approaching Pareto optimal front by searching Pareto optimal tree, Tech. Report Nk-CS-200, 1–2, (2001).
- Moura, P. S. ve de Almeida, A. T., Multi-objective optimization of a mixed renewable system with demand-side management, Renewable & Sustainable Energy Reviews, 14, 5, 1461–1468, (2010).
- Demirtas, M., Ilten, E., Calgan, H., Pareto-Based Multi-objective Optimization for Fractional Order PIλ Speed Control of Induction Motor by Using Elman Neural Network, Arabian Journal for Science and Engineering, https://doi.org/10.1007/s13369-018-3364-2.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Aralık 2018 |
| Gönderilme Tarihi | 6 Ocak 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 20 Sayı: 2 |