Design and Performance Analyze of PID/PD Controller to Perform Speed Control of Brushless DC Motors

Abstract

The usage of brushless DC motors (BLDC) is increasing day by day due to its advantages such as providing constant mechanical torque and high power density. As the use of BLDC motors becomes widespread, a number of new studies have been introduced in terms of control of these motors In our study, firstly the PID control of the BLDC motor was performed to reduce the values such as the maximum overshoot and the settling time at the output, and the controller's parameters were obtained by the Whale Optimization Algorithm (WOA). The results were compared with the results of the system with the same parameters of the BLDC motor in the literature using PID controllers optimized by the genetic algorithm (GA), particle swarm optimization (PSO), LQR and LQ methods. In addition, PID/PD controller was utilized instead of PID for the same system and WOA was again used to determine parameters. The system designed with PID/PD controller has been compared with the system designed with PID controller systems in terms of performance criteria such as maximum overshoot, settling time and rise time. Then, in order to perform the dynamic test of both PID and PID/PD controllers, the speed of the motor was increased and the results were obtained, examined and compared with each other. As a result, it was observed that BOA-PID performed better than PID results obtained by other methods, while BOA-PID PD performed better than all studies using PID.

Keywords

• Brushless DC motor,PID control,PID/PD control,Whale optimization algorithm,Determination of parameters

Fırçasız Doğru Akım Motorlarının Hız Kontrolünü Gerçekleştirmek İçin PID/PD Kontrolcü Tasarımı ve Performans İncelemesi

Öz

Fırçasız doğru akım (FDA) motorlarının kullanımı, düzgün mekanik tork sağlaması ve yüksek güç yoğunluğuna sahip olması gibi avantajlarından dolayı günden güne artmaktadır. FDA motorlarının kullanımı yaygınlaştıkça bu motorların kontrolü ile ilgili yeni çalışmalar ortaya konmaktadır. Bizim çalışmamızda, çıkıştaki maksimum aşma miktarı ve oturma zamanı gibi değerlerin düşürülmesi amacıyla ilk olarak FDA motorun PID kontrolü yapılmış ve kontrolcünün parametreleri Balina Optimizasyon Algoritması (BOA) ile elde edilmiştir. Elde edilen sonuçlar literatürde aynı parametrelere sahip FDA motorun genetik algoritma (GA), parçacık sürü optimizasyonu (PSO), LQR ve LQ yöntemleri ile optimize edilen PID kontrolcülerinin kullanıldığı sistemin sonuçları ile karşılaştırılmıştır. Bunun yanında PID yerine PID/PD kontrolcü kullanılarak aynı sistemin kontrolü tekrar yapılmış ve parametrelerin belirlenmesi için yine BOA’dan faydalanılmıştır. PID/PD kontrolcü ile tasarlanan sistem; maksimum aşma, oturma zamanı ve yükselme zamanı gibi performans kriterleri açısından PID kontrolcülü sistemlerle karşılaştırılmıştır. Ardından hem PID hem de PID/PD kontrolcülerin dinamik testini gerçekleştirmek amacıyla motorun hızı artırılarak sonuçlar elde edilmiş, irdelenmiş ve birbirleri ile karşılaştırılmıştır. Sonuçlara bakıldığında ise BOA-PID’nin diğer yöntemlerle elde edilen PID sonuçlarından daha iyi performans gösterdiği, BOA-PID/PD’nin ise PID’nin kullanıldığı tüm çalışmalardan daha iyi performans sergilediği görülmüştür.

Anahtar Kelimeler

• PID kontrol,PID/PD kontrol,Fırçasız DA motoru,Balina optimizasyon algoritması,Parametrelerin belirlenmesi

Kaynakça

1. Ai, L., Ramachandaramurthy, V. K., Walker, S. L., & Taylor, P. (2019). Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm. Journal of Energy Storage, 26(May), 100892. https://doi.org/10.1016/j.est.2019.100892
2. 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
3. Anwar, M. N., & Pan, S. (2013). Synthesis of the PID controller using desired closed-loop response. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 10). https://doi.org/10.3182/20131218-3-IN-2045.00023
4. Cao, Y., Li, Y., Zhang, G., Jermsittiparsert, K., & Nasseri, M. (2020). An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Reports, 6, 530–542. https://doi.org/10.1016/j.egyr.2020.02.035
5. Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., McKenna, M. F., Simon, M., & Nowacek, D. P. (2013). Integrative Approaches to the Study of Baleen Whale Diving Behavior, Feeding Performance, and Foraging Ecology. BioScience, 63(2), 90–100. https://doi.org/10.1525/bio.2013.63.2.5
6. GRIGORIE, T. L., KHAN, S., BOTEZ, R. M., MAMOU, M., & MÉBARKI, Y. (2019). Design and experimental testing of a control system for a morphing wing model actuated with miniature BLDC motors. Chinese Journal of Aeronautics. https://doi.org/10.1016/j.cja.2019.08.007
7. Hajİsalm, A., & Altaş, İ. H. (2014). Hibrit Rüzgar / F V Enerji Sistemleri İçin P ID Denetleyici Parametrelerinin PSO ve GA ile Optimizasyonu Optimization of PID Controller Parameters in Wind / PV Energy Systems Using PSO and GA Elektrik - Elektronik Mühendisliği Bölümü Elektrik - Elektronik. 27–29.
8. 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

9. Kim, C. H., Yang, J. H., Lim, D. G., & Suh, B. S. (2010). An enhanced PID controller for speed control of brushless DC motors based on convex set optimization. IFAC Proceedings Volumes (IFAC-PapersOnline), 8(PART 1), 75–80. https://doi.org/10.3182/20100929-3-ro-4017.00014
10. Kumar, B., Swain, S. K., & Neogi, N. (2017). Controller design for closed loop speed control of BLDC motor. International Journal on Electrical Engineering and Informatics, 9(1), 146–160. https://doi.org/10.15676/ijeei.2017.9.1.10
11. Long, W., Wu, T., Jiao, J., Tang, M., & Xu, M. (2020). Engineering Applications of Artificial Intelligence Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model ✩. Engineering Applications of Artificial Intelligence, 89(November 2019), 103457. https://doi.org/10.1016/j.engappai.2019.103457
12. Lu, H., Zhang, L., & Qu, W. (2008). A new torque control method for torque ripple minimization of BLDC motors with un-ideal back EMF. IEEE Transactions on Power Electronics, 23(2), 950–958. https://doi.org/10.1109/TPEL.2007.915667
13. Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
14. MOHAN, N., UNDELAND, T. M., & ROBBINS, W. (2007). Güç Elektroniği Çeviriciler, Uygulamalar ve Tasarım (2. Baskı). Literatür Yayıncılık, Dağıtım, Pazarlama San. ve Tic. Ltd. Şti.
15. Nasiri, J., & Khiyabani, F. M. (2018). A whale optimization algorithm (WOA) approach for clustering. Cogent Mathematics & Statistics, 5(1), 1–13. https://doi.org/10.1080/25742558.2018.1483565
16. Shekhar, S., Saha, P. K., & Thakura, P. R. (2020). Optimal PID Tuning of BLDC Drive using LQR Technique. 2, 57–574. https://doi.org/10.1109/icisgt44072.2019.00028
17. Siostrzonek, T., & Pirog, S. (2007). Motor the Practical Results1. 1541–1545.
18. Syed Hussien, S. Y., Jaafar, H. I., Ghazali, R., & Abdul Razif, N. R. (2015). The effects of auto-tuned method in PID and PD control scheme for gantry crane system. International Journal of Soft Computing and Engineering (IJSCE), (6), 121–125. Retrieved from http://eprints.utem.edu.my/14047/1/%5B1%5D_F2492014615.pdf
19. Tabak, A., Kayabasi, E., Guneser, M. T., & Ozkaymak, M. (2019). Grey wolf optimization for optimum sizing and controlling of a PV/WT/BM hybrid energy system considering TNPC, LPSP, and LCOE concepts. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2019.1668880
20. Tabak, A., Özkaymak, M., Tahir, M., & Oktay, H. (2017). Optimization and Evaluation of Hybrid PV/WT/BM System in Different Initial Costs and LPSP Conditions. International Journal of Advanced Computer Science and Applications, 8(11), 123–131. https://doi.org/10.14569/ijacsa.2017.081116
21. 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
22. Uysal, O. (2010). Ekzotermik (Isı Yayan) Kimyasal Reaktörü Simüle Eden Bir Isıl Sistemin Dinamik Davranışının İncelenmesi. İstanbul Teknik Üniversitesi.
23. Xu, F., Li, T., & Tang, P. (2008). A Low Cost Drive Strategy for BLDC motor with low torque ripples. 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, 1, 2499–2502. https://doi.org/10.1109/ICIEA.2008.4582968
24. Yu, G. R., & Hwang, R. C. (2004). Optimal PID speed control of brush less DC motors using LQR approach. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1, 473–478. https://doi.org/10.1109/ICSMC.2004.1398343
25. Zhang, H., Tang, L., Yang, C., & Lan, S. (2019). Advanced Engineering Informatics Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm. Advanced Engineering Informatics, 41(May 2018), 100901. https://doi.org/10.1016/j.aei.2019.02.006
26. Zhang, Y., Zhang, L., & Dong, Z. (2019). An MEA-Tuning Method for Design of the PID Controller. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/1378783