DETERMINATION OF PI COEFFICIENTS IN SPEED CONTROL OF BRUSHLESS DC MOTOR WITH GRAY WOLF OPTIMIZATION AND FPGA APPLICATION

Yıl 2024, Cilt: 27 Sayı: 3, 1044 – 1056, 03.09.2024

Yurdagül Benteşen Yakut

https://doi.org/10.17780/ksujes.1457598

Öz

Doğru akım (DC) motorları, verimlilikleri, uzun ömürleri ve ayarlanabilir hız özellikleri nedeniyle birçok endüstride yaygın olarak kullanılmaktadır. Bu motorların etkin bir şekilde kontrolü, geniş kullanım alanları göz önüne alındığında son derece önemli oldukları görülmektedir. Uygulama alanları değiştikçe, kontrol edilen motor parametreleri de farklılık göstermekte ve bu nedenle sanayi kullanımına uygun kontrol sistemlerinin geliştirilmesi gerekmektedir. Bununla birlikte, standart kontrolörler, genellikle matematiksel modellerin doğrusal olmayan ve belirsiz yapısı nedeniyle zorluklarla karşılaşmaktadır. Bu çalışma, fırçasız DC motor hız kontrolünde PI katsayılarının belirlenmesi amacıyla Gri Kurt Optimizasyonu (GKO) yöntemini kullanarak yeni bir yaklaşım sunmayı hedeflemektedir ve bu yöntem, bir FPGA üzerinde uygulanmıştır. Çalışma sürecinde, BLDC motor için bir kontrol stratejisi modeli MATLAB/Simulink kullanılarak geliştirilmiştir. Motorun hızı, kontrolör katsayılarını hesaplamak amacıyla belirli aralıklarla 300 rpm’den 600 ve 900 rpm’ye kademeli olarak artırılmıştır. GKO tekniği, ITAE maliyet fonksiyonunu kullanarak PI parametreleri olan Kp ve Ki’yi optimize etmiştir. Sonuçlar, geleneksel PI ve GKO-PI kontrolörlerinin referans hız ile karşılaştırılmasında, GKO-PI’nin daha yakın bir uyum sağladığını göstermiştir. Çoğu çalışmanın simülasyonlara odaklanmasının aksine, bu araştırma modeli donanım üzerinde test etmiştir ve özellikle BASYS3 FPGA eğitim kartı kullanılarak BLDC motorun sanayi ortamında daha yüksek hızlarda çalışabileceği optimize edilmiş GKO-PI yöntemi ile gösterilmiştir.

Anahtar Kelimeler

Fırçasız DC motor, FPGA, GWO-PI, Hız kontrolü

Kaynakça

• Abro, K. A., Atangana, A. & Gómez-Agui̇lar, J. (2022). Chaos control and characterization of brushless DC motor via integral and differential fractal-fractional techniques. Internati̇onal Journal Of Modelli̇ng and Si̇mulati̇on, 43(4), 416-425. https://doi.org/10.1080/02286203.2022.2086743
• Ahmed, S. & Yahi̇a, K. (2024). Implementation of fuzzy logic controller algorithm with mf optimization on FPGA. Stati̇sti̇cs, Opti̇mi̇zati̇on and Informati̇on Computi̇ng, (12), 182-199. DOI: 10.19139/soic-2310-5070-1790
• Ansari, U., Alam, S., & Jafri, S. M. U. N., (2011). Modeling and control of three phase BLDC motor using PID with genetic algorithm. Proceedings – 2011 UKSim 13th International Conference on Modelling and Simulation, UKSim 2011, 189–194. https://doi.org/10.1109/UKSIM.2011.44
• Anti̇c, S., Lukovi̇c, V., Rosi̇c, M. & Pesovi̇c, U. (2023, June). FPGA digital circuit for actuator and sensor FDI of DC motor with an amplifier. International Conference on Electrical Electronics and Computer Engineering (ICETRAN). East Sarajevo.
• Anwar, M. N., Pan, S., (2013). Synthesis of the PID controller using desired closed-loop response. 10th IFAC International Symposium on Dynamics and Control of Process Systems, 46 (32), 385-390.
• Arserim, M. A. Haydaroğlu, C., Acar, H. & Uçar, A. (2019). Forming and co-simulation of square and triangular waveforms by using system generator. Balkan Journal of Electri̇cal & Computer Engi̇neeri̇ng, 7(3), 337-341. DOI: 10.17694/bajece.505842
• Banerjee, S., Kumar, S. S., Alam, A., (2022). Whale Optimization Algorithm (WOA) Based Speed Control of BLDC Motor. 2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT), Hyderabad, India, 2022, pp. 1-6, doi: 10.1109/SeFeT55524.2022.9909419.
• Bharatkar, S.S., Yanamshetti, R., Chatterjee, D., & Ganguli, A.K. (2011). Dual-Mode Switching Technique for Reduction of Commutation Torque Ripple of Brushless Dc Motor. IET Electr. Power Appl., 5, 193–202 Digilent, https://digilent.com/shop/basys-3-artix-7-fpga-trainerboard-recommended-for-introductoryusers (Accessed 18.08.2022).
• Gökbulut, M, Dandil, B, Bal, C., (2006). A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors. Editor: Savacı FA. Artificial Intelligence and Neural Networks, 125-132, Springer Berlin Heidelberg.
• Hooshmand, M., Yaghobi̇, H. & Jazaeri̇, M. (2023). Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter. Electri̇cal Engi̇neeri̇ng, 105, 1797–1810. https://doi.org/10.1007/s00202-023-01773-y
• Ibrahim, H. E. A., Hassan, F. N., & Shomer, A. O., (2014). Optimal PID control of a brushless DC motor using PSO and BF techniques. Ain Shams Engineering Journal, 5(2), 391–398. https://doi.org/10.1016/j.asej.2013.09.013
• Inti̇dam, A., Fadi̇l, H. E., Housny, H., Idri̇ssi̇, Z. E., Lassi̇oui̇, A., Nady, S. & Laafouabdeslam, A. J. (2023). Development and experimental implementation of optimized PI-ANFIS controller for speed control of a brushless DC motor in fuel cell electric vehicles. Energi̇es, 4396 (16), 1-23. https://doi.org/ 10.3390/en16114395
• Jin Y, Tang Z, Wen Y, Zou, H., (2006). High performance adaptive control for BLDC motor with realtime estimation of uncertainties. 21th Annual IEEE Applied Power Electronics Conference and Exposition, Dallas, TX, USA, 19-23
• Krishnan, R. (2017). Switched Reluctance Motor Drives: Modeling, Simulation, Analysis. Design, and Applications; CRC Press: Boca Raton, FL, USA, ISBN 1315220067.
• Liu, Y, Zhao, J, Xia, M, Luo, H., (2014). Model reference adaptive control-based speed control of brushless DC motors with low-resolution Hall-effect sensors. IEEE Transactions on Power Electronics, 29(3), 1514-1522.
• Masoudi̇, H., Ki̇youmarsi̇, A., Madani̇, S. M. & Ataei̇, M. (2023). Closed-loop direct power control of brushless dc motor in field weakening region. IEEE Transacti̇ons On Transportati̇on Electri̇fi̇cati̇on.
• Miller, T.J.E. (1989). Brushless Permanent-Magnet and Reluctance Motor Drives. Clarendon Press: Oxford, UK. Mi̇rjali̇li̇, S. & Lewi̇s, A. (2014). Grey wolf optimizer. Advances in Engi̇neeri̇ng Software, 69, 41-46. http://dx.doi.org/10.1016/j.advengsoft.2013.12.007
• Mi̇rjali̇li̇, S. (2015). How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell, 43, 150-161. DOI 10.1007/s10489-014-0645-7
• Muniraj, C., Kamatchi, K.V., Peri̇asamy, B., Karthikeyan, G., Deepak, S. & Muhammed, M. (2023). Experimental ımplementation of speed control of a brushless DC motor using FPGA. Proceedings of the Second International Conference on Automation, Computing and Renewable Systems (ICACRS), IEEE.
• Nasri̇, M., Nezamabadi̇-Pour, H. & Maghfoori̇, M. (2007). A PSO-based optimum design of PID controller for a linear brushless DC motor. Internati̇onal Journal of Electri̇cal and Informati̇on Engi̇neeri̇ng, 1(2), 171-175. scholar.waset.org/1307-6892/10876
• Padula, F. & Visioli, A. (2011). Tuning rules for optimal PID and Fractional-order PID controllers. Journal of Process Control, Cilt: 21, No:1, s:69-81.
• Premkumar, K, Manikandan, B.V., (2014). Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor. Neurocomputing, 138, 260-270.
• Ramakri̇shnan, A., Shunmugalatha, A. & Premkumar, K. (2023). An improved tuning of PIDcontroller for pv battery-powered brushless DC motor speed regulation using hybrid horse herd particle swarm optimization. Internati̇onal Journal of Photoenergy, 2777505, pp:1-23. https://doi.org/10.1155/2023/2777505
• Shai̇kh, M. S., Hua, C., Jatoi̇, M. A., Ansari̇, M. M. & Qader A. A. (2021). Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system. IET Sci̇ence, Measurement & Technology, (15), 218-231. DOI: 10.1049/smt2.12023
• Shary, D. K., Nekad, H. J. & Alawan, M. A. (2023). Speed control of brushless dc motors using (conventional, heuristic, and intelligent) methods-based PID controllers. Indonesi̇an Journal of Electri̇cal Engi̇neeri̇ng And Computer Sci̇ence, 30 (3), 1359-1368. 10.11591/ijeecs.v30.i3
• Tarczewski, T., & Grzesiak, L. M., (2018). An Application of Novel Nature-Inspired Optimization Algorithms to Auto-Tuning State Feedback Speed Controller for PMSM. IEEE Transactions on Industry Applications, 54(3), 2913–2925. https://doi.org/10.1109/TIA.2018.2805300
• Udayakumar, A. K., Raghavan, R. R. V., Houran, M. A., Elavarasan, R. M., Kalavathy, A. N. & Hossai̇n, E. (2023). Three-port bi-directional DC-DC converter with solar pv system fed BLDC motor drive using FPGA. Energi̇es, 624 (16), pp: 1-21. https://doi.org/10.3390/en16020624
• Usman, A. & Rajpurohi̇t, B. S. (2020). Design and control of a BLDC motor drive using hybrid modeling technique and FPGA based hysteresis current controller. IEEE, Indi̇a.
• Wang, H., Chau, T., Li̇u, W. & Goetz, S. M. (2023). Design and control of wireless permanent-magnet brushless DC motors. IEEE Transacti̇ons on Energy Conversi̇on, 38(4), 2969-2979. https://doi.org/10.1109/TEC.2023.3292178.
• Wang, H.P., Liu Y.T., (2006). Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor. IEEE Transactions on Power Electronics, 21(2), 518-523.
• Wang, Y, Xia, C, Zhang, M, Liu D., (2007). Adaptive speed control for brushless DC motors based on genetic algorithm and RBF neural network”. 2007 IEEE International Conference on Control and Automation, Guangzhou, China.
• Yorat, E., Özbek, N. S. & Sarıbulut, L. (2023). Fırçasız doğru akım motor kontrol yöntemlerinin düşük maliyetli mikrodenetleyici tabanlı gerçek zamanlı deneylerle performans değerlendirmesi. Gazi̇ Üni̇versi̇tesi Fen Bi̇li̇mleri̇ Dergi̇si̇, 11(2), 498-510. 10.29109/gujsc.1229896
• Younus, S. M. Y., Kutbay, U. & Rahebi̇, J. F. H. (2023). Hybrid gray wolf optimization–Proportional integral based speed controllers for brush-less dc motor. Energi̇es, 1640(16), 1-18. https://doi.org/10.3390/en16041640
• Zhou, Y. (2022). A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems. Security and Communication Networks, vol. Article ID 7156713, 7 pages

DETERMINATION OF PI COEFFICIENTS IN SPEED CONTROL OF BRUSHLESS DC MOTOR WITH GRAY WOLF OPTIMIZATION AND FPGA APPLICATION

Yıl 2024, Cilt: 27 Sayı: 3, 1044 – 1056, 03.09.2024

Yurdagül Benteşen Yakut

https://doi.org/10.17780/ksujes.1457598

Öz

DC motors are widely utilized in various industries due to their efficiency, longevity, and adjustable speed settings. Effective control of these motors is crucial, given their broad application range. As applications vary, so do the controlled motor parameters, necessitating control systems that are suitable for industrial use. However, standard controllers often face challenges due to the non-linear and uncertain nature of the mathematical models involved. This study aims to introduce a novel approach by employing Grey Wolf Optimization (GWO) to determine the PI coefficients for brushless DC motor speed control, which is then implemented on an FPGA. During the study, a control strategy model for the BLDC motor was developed using MATLAB/Simulink. The motor’s speed was gradually increased from 300 to 600 and 900 rpm at specific intervals to calculate the controller coefficients. The GWO technique optimized the PI parameters, Kp and Ki, using the ITAE cost function. The results showed an improvement in speed control when comparing the conventional PI and GWO-PI controllers to the reference speed, with GWO-PI achieving closer adherence. As opposed to most studies that focus on simulations, this research tested the model using hardware, specifically the BASYS3 FPGA training card, demonstrating that the BLDC motor can operate at higher speeds in industrial settings with the optimized GWO-PI approach.

Anahtar Kelimeler

Brushless DC motor, FPGA, GWO-PI, Speed Control

Kaynakça

• Abro, K. A., Atangana, A. & Gómez-Agui̇lar, J. (2022). Chaos control and characterization of brushless DC motor via integral and differential fractal-fractional techniques. Internati̇onal Journal Of Modelli̇ng and Si̇mulati̇on, 43(4), 416-425. https://doi.org/10.1080/02286203.2022.2086743
• Ahmed, S. & Yahi̇a, K. (2024). Implementation of fuzzy logic controller algorithm with mf optimization on FPGA. Stati̇sti̇cs, Opti̇mi̇zati̇on and Informati̇on Computi̇ng, (12), 182-199. DOI: 10.19139/soic-2310-5070-1790
• Ansari, U., Alam, S., & Jafri, S. M. U. N., (2011). Modeling and control of three phase BLDC motor using PID with genetic algorithm. Proceedings – 2011 UKSim 13th International Conference on Modelling and Simulation, UKSim 2011, 189–194. https://doi.org/10.1109/UKSIM.2011.44
• Anti̇c, S., Lukovi̇c, V., Rosi̇c, M. & Pesovi̇c, U. (2023, June). FPGA digital circuit for actuator and sensor FDI of DC motor with an amplifier. International Conference on Electrical Electronics and Computer Engineering (ICETRAN). East Sarajevo.
• Anwar, M. N., Pan, S., (2013). Synthesis of the PID controller using desired closed-loop response. 10th IFAC International Symposium on Dynamics and Control of Process Systems, 46 (32), 385-390.
• Arserim, M. A. Haydaroğlu, C., Acar, H. & Uçar, A. (2019). Forming and co-simulation of square and triangular waveforms by using system generator. Balkan Journal of Electri̇cal & Computer Engi̇neeri̇ng, 7(3), 337-341. DOI: 10.17694/bajece.505842
• Banerjee, S., Kumar, S. S., Alam, A., (2022). Whale Optimization Algorithm (WOA) Based Speed Control of BLDC Motor. 2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT), Hyderabad, India, 2022, pp. 1-6, doi: 10.1109/SeFeT55524.2022.9909419.
• Bharatkar, S.S., Yanamshetti, R., Chatterjee, D., & Ganguli, A.K. (2011). Dual-Mode Switching Technique for Reduction of Commutation Torque Ripple of Brushless Dc Motor. IET Electr. Power Appl., 5, 193–202 Digilent, https://digilent.com/shop/basys-3-artix-7-fpga-trainerboard-recommended-for-introductoryusers (Accessed 18.08.2022).
• Gökbulut, M, Dandil, B, Bal, C., (2006). A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors. Editor: Savacı FA. Artificial Intelligence and Neural Networks, 125-132, Springer Berlin Heidelberg.
• Hooshmand, M., Yaghobi̇, H. & Jazaeri̇, M. (2023). Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter. Electri̇cal Engi̇neeri̇ng, 105, 1797–1810. https://doi.org/10.1007/s00202-023-01773-y
• Ibrahim, H. E. A., Hassan, F. N., & Shomer, A. O., (2014). Optimal PID control of a brushless DC motor using PSO and BF techniques. Ain Shams Engineering Journal, 5(2), 391–398. https://doi.org/10.1016/j.asej.2013.09.013
• Inti̇dam, A., Fadi̇l, H. E., Housny, H., Idri̇ssi̇, Z. E., Lassi̇oui̇, A., Nady, S. & Laafouabdeslam, A. J. (2023). Development and experimental implementation of optimized PI-ANFIS controller for speed control of a brushless DC motor in fuel cell electric vehicles. Energi̇es, 4396 (16), 1-23. https://doi.org/ 10.3390/en16114395
• Jin Y, Tang Z, Wen Y, Zou, H., (2006). High performance adaptive control for BLDC motor with realtime estimation of uncertainties. 21th Annual IEEE Applied Power Electronics Conference and Exposition, Dallas, TX, USA, 19-23
• Krishnan, R. (2017). Switched Reluctance Motor Drives: Modeling, Simulation, Analysis. Design, and Applications; CRC Press: Boca Raton, FL, USA, ISBN 1315220067.
• Liu, Y, Zhao, J, Xia, M, Luo, H., (2014). Model reference adaptive control-based speed control of brushless DC motors with low-resolution Hall-effect sensors. IEEE Transactions on Power Electronics, 29(3), 1514-1522.
• Masoudi̇, H., Ki̇youmarsi̇, A., Madani̇, S. M. & Ataei̇, M. (2023). Closed-loop direct power control of brushless dc motor in field weakening region. IEEE Transacti̇ons On Transportati̇on Electri̇fi̇cati̇on.
• Miller, T.J.E. (1989). Brushless Permanent-Magnet and Reluctance Motor Drives. Clarendon Press: Oxford, UK. Mi̇rjali̇li̇, S. & Lewi̇s, A. (2014). Grey wolf optimizer. Advances in Engi̇neeri̇ng Software, 69, 41-46. http://dx.doi.org/10.1016/j.advengsoft.2013.12.007
• Mi̇rjali̇li̇, S. (2015). How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell, 43, 150-161. DOI 10.1007/s10489-014-0645-7
• Muniraj, C., Kamatchi, K.V., Peri̇asamy, B., Karthikeyan, G., Deepak, S. & Muhammed, M. (2023). Experimental ımplementation of speed control of a brushless DC motor using FPGA. Proceedings of the Second International Conference on Automation, Computing and Renewable Systems (ICACRS), IEEE.
• Nasri̇, M., Nezamabadi̇-Pour, H. & Maghfoori̇, M. (2007). A PSO-based optimum design of PID controller for a linear brushless DC motor. Internati̇onal Journal of Electri̇cal and Informati̇on Engi̇neeri̇ng, 1(2), 171-175. scholar.waset.org/1307-6892/10876
• Padula, F. & Visioli, A. (2011). Tuning rules for optimal PID and Fractional-order PID controllers. Journal of Process Control, Cilt: 21, No:1, s:69-81.
• Premkumar, K, Manikandan, B.V., (2014). Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor. Neurocomputing, 138, 260-270.
• Ramakri̇shnan, A., Shunmugalatha, A. & Premkumar, K. (2023). An improved tuning of PIDcontroller for pv battery-powered brushless DC motor speed regulation using hybrid horse herd particle swarm optimization. Internati̇onal Journal of Photoenergy, 2777505, pp:1-23. https://doi.org/10.1155/2023/2777505
• Shai̇kh, M. S., Hua, C., Jatoi̇, M. A., Ansari̇, M. M. & Qader A. A. (2021). Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system. IET Sci̇ence, Measurement & Technology, (15), 218-231. DOI: 10.1049/smt2.12023
• Shary, D. K., Nekad, H. J. & Alawan, M. A. (2023). Speed control of brushless dc motors using (conventional, heuristic, and intelligent) methods-based PID controllers. Indonesi̇an Journal of Electri̇cal Engi̇neeri̇ng And Computer Sci̇ence, 30 (3), 1359-1368. 10.11591/ijeecs.v30.i3
• Tarczewski, T., & Grzesiak, L. M., (2018). An Application of Novel Nature-Inspired Optimization Algorithms to Auto-Tuning State Feedback Speed Controller for PMSM. IEEE Transactions on Industry Applications, 54(3), 2913–2925. https://doi.org/10.1109/TIA.2018.2805300
• Udayakumar, A. K., Raghavan, R. R. V., Houran, M. A., Elavarasan, R. M., Kalavathy, A. N. & Hossai̇n, E. (2023). Three-port bi-directional DC-DC converter with solar pv system fed BLDC motor drive using FPGA. Energi̇es, 624 (16), pp: 1-21. https://doi.org/10.3390/en16020624
• Usman, A. & Rajpurohi̇t, B. S. (2020). Design and control of a BLDC motor drive using hybrid modeling technique and FPGA based hysteresis current controller. IEEE, Indi̇a.
• Wang, H., Chau, T., Li̇u, W. & Goetz, S. M. (2023). Design and control of wireless permanent-magnet brushless DC motors. IEEE Transacti̇ons on Energy Conversi̇on, 38(4), 2969-2979. https://doi.org/10.1109/TEC.2023.3292178.
• Wang, H.P., Liu Y.T., (2006). Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor. IEEE Transactions on Power Electronics, 21(2), 518-523.
• Wang, Y, Xia, C, Zhang, M, Liu D., (2007). Adaptive speed control for brushless DC motors based on genetic algorithm and RBF neural network”. 2007 IEEE International Conference on Control and Automation, Guangzhou, China.
• Yorat, E., Özbek, N. S. & Sarıbulut, L. (2023). Fırçasız doğru akım motor kontrol yöntemlerinin düşük maliyetli mikrodenetleyici tabanlı gerçek zamanlı deneylerle performans değerlendirmesi. Gazi̇ Üni̇versi̇tesi Fen Bi̇li̇mleri̇ Dergi̇si̇, 11(2), 498-510. 10.29109/gujsc.1229896
• Younus, S. M. Y., Kutbay, U. & Rahebi̇, J. F. H. (2023). Hybrid gray wolf optimization–Proportional integral based speed controllers for brush-less dc motor. Energi̇es, 1640(16), 1-18. https://doi.org/10.3390/en16041640
• Zhou, Y. (2022). A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems. Security and Communication Networks, vol. Article ID 7156713, 7 pages

Toplam 34 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

Download or read online: Click here