Fırçasız DC Motorun Denge Optimizasyon Algoritması Tabanlı FOPID Kontrolü

Öz

PID kontrolün ana zorlukları, zayıf yanıta yol açan ani ayar noktası değişiklikleri ve parametre değişiklikleridir. Bu kontrol ünitesinin FOPID olarak bilinen başka bir benzer kontrol ünitesi ile değiştirilebileceği dikkate alınabilmektedir. Ancak entegrasyon ve farklılaşma derecesinde ondan farklıdır ve bu, geçici durumda sistemin performansını artırmaktadır. FOPID katsayılarını seçmek için, mümkün olan en iyi performansı elde etmek için optimizasyon algoritmaları da dahil olmak üzere çeşitli metodolojiler kullanılmaktadır. Bu makalede fırçasız DC motor (BLDC) hız kontrolü FOPID yapılmıştır. Kontrolör parametrelerini belirlemek için EO algoritması uygulanmıştır ve bu algoritmanın performansı, PSO, DE ve GJO gibi diğer optimizasyon algoritmalarıyla karşılaştırılmıştır. Matlab-Simulink 2016a'daki simülasyon sonuçları, önerilen algoritmanın (EO) diğer algoritmalara kıyasla daha iyi tepki süresi, aşım ve daha düşük kararlı hal hatası elde etmedeki etkinliğini göstermektedir.

Anahtar Kelimeler

• Equilibrium Optimizer
• FOPID
• PID
• BLDC motor.

Equilibrium Optimizer Based FOPID Control of BLDC Motor

Abstract

The main challenges of proportional integral derivative (PID) control are sudden set-point changes and parameter changes, which leads to poor response. It can be taken into account that this control unit can be replaced by another similar control unit, but it differs from it in the degree of integration and differentiation, and this is what is known as fractional-order PID (FOPID), which improves the performance of the system in the transient state. To choose the FOPID constants, various methodologies, including optimization algorithms, are used to obtain the best possible performance. In this paper, the speed of brushless DC motor (BLDC) was regulated using (FOPID), where the equilibrium optimizer (EO) algorithm was used to find the values of the controller constants, and the performance of this algorithm was compared with several other optimization algorithms such as particle swarm optimization (PSO), differential evolution (DE), and golden jackal optimization (GJO). Simulation results in Matlab-Simulink 2016a showed the effectiveness of the proposed algorithm (EO) in achieving response time, overshot, and lower steady state error compared with the rest of the algorithms.

Keywords

• Equilibrium Optimizer
• FOPID
• PID
• BLDC motor

References

1. Denizci, A., & Ulu, C. (2020). Fuzzy Cognitive Map Based PID Controller Design. European Journal of Science and Technology, Speciel Issue, 165-171.
2. Dubey, S. M., Dubey, H. M., Pandit, M., & Salkuti, S. R. (2021). Multiobjective Scheduling of Hybrid Renewable Energy System Using Equilibrium Optimization. Energies, 14 (19), 6376.
3. El-Zohri, E. H., & Mosbah, M. A. (2020, February). Speed control of inverter-fed induction motor using hybrid fuzzy-PI controller. In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), Aswan, Egyypt, 216-221.
4. Euldji, R., Batel, N., Redha, R., Noureddine K., et al. (2022). Optimal Backstepping-FOPID Controller Design for Wheeled Mobile Robot. Journal Europeen des Systemes Automatises, 55 (1), 97-107.
5. Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium Optimizer: A Novel Optimization Algorithm. Knowledge-Based Systems, 191, 105190.
6. Hannan, M. A., Abd Ali, J., Ker, P. J., Mohamed, A., Lipu, M. S., & Hussain, A. (2018). Switching Techniques and Intelligent Controllers for Induction Motor Drive: Issues and Recommendations. IEEE Access, 6, 47489-47510.
7. Houssein, E. H., Nageh, G., Abd Elaziz, M., & Younis, E. (2021). An Efficient Equilibrium Optimizer for Parameters Identification of Photovoltaic Modules. PeerJ Computer Science, 9 (7), e708.
8. Jamil, A. A., Tu, W. F., Ali, S. W., Terriche, Y., & Guerrero, J. M. (2022). Fractional-Order PID Controllers for Temperature Control: A Review. Energies, 15 (10), 3800.

9. Köse, O., & Oktay, T. (2020). Investigation of the Effect of Differential Morphing on Lateral Flight by Using PID Algorithm in Quadrotors. European Journal of Science and Technology,18, 636-644.
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.
11. Lavanya, Y., Bhavani, N. P. G., Ramesh, N., & Sujatha, K. (2015). Sensorless Vector Control of BLDC using Extended Kalman Filter. Signal & Image Processing: An International Journal (SIPIJ), 6 (3), 103-114.
12. Najib, M. S., Jadin, M. S., Ismail, R. M. T. R., & Mohamed, M. R. (2007, October). Design and Implementation of PID Controller in Programmable Logic Controller for DC Motor Position Control of the Conveyor System. In Proceedings of the 3rd WSEAS/IASME International Conference on Dynamical Systems and Control, Arcachon, France, 266-270.
13. Shamseldin, M. A., & EL-Samahy, A. A. (2014, September). Speed Control of BLDC Motor by using PID Control and Self-Tuning Fuzzy PID Controller. In 15th International Workshop on Research and Education in Mechatronics (REM), El Gouna, Egypt, 1-9.
14. Singh, A. P., Narayan, U., & Verma, A. (2013). Speed Control of DC Motor using PID Controller Based on Matlab. Innovative Systems Design and Engineering, 4 (6), 22-28.
15. Tepljakov, A., Alagoz, B. B., Yeroglu, C., Gonzalez, E., HosseinNia, S. H., & Petlenkov, E. (2018). FOPID Controllers and Their Industrial Applications: A Survey of Recent Results. IFAC-PapersOnLine, 51 (4), 25-30.
16. Xue, D., Zhao, C., & Chen, Y. (2006, June). Fractional order PID Control of a DC-Motor with Elastic Shaft: A Case Study. In 2006 American Control Conference, Minnesota, USA, 3182-3187.
17. Yang, B., Yu, T., Shu, H., Han, Y., Cao, P., & Jiang, L. (2019). Adaptive Fractional Order PID Control of PMSG based Wind Energy Conversion System for MPPT using Linear Observers. International Transactions on Electrical Energy Systems, 29 (1), e2697.