This is an open access article distributed under the CC BY 4.0
Volume 19 article 868 pages: 902-909
BLDC motor is the most widely used in the industrial world, especially in electric vehicles. With this increasing demand, a variety of research topics emerged in BLDC motors. One popular research is on BLDC motor speed control topics to maintain speed for its application, such as intelligent cruise technology in electric cars and conveyors for line assembly. However, from several existing studies, the BLDC Motor controller still uses a single controller model. The controller's output is purely from the controller without any improvement in characteristics and has a problem with the oscillating speed setpoint (error problem). In this study, the researcher proposed a combining control with the concept of summation output to handle this problem. With this concept, the control techniques used can improve each other so that better control can be produced following the control system assessment parameters. The authors used a Fuzzy Logic Controller, Artificial Neural Network (ANN), and PID, which were combined and obtained seven control systems. The results show that the control system can improve several parameters using the summation concept from the seven controllers model. It has a positive overall correlation when viewed in terms of the difference between the Error and the setpoint or MAE (Mean Absolute Error) as parameter assessment.
1. Joseph Godfrey, A., & Sankaranarayanan, V. (2018). A new electric braking system with energy regeneration for a BLDC motor driven electric vehicle. Engineering Science and Technology, an International Journal, 21(4), 704–713. https://doi.org/10.1016/j.jestch.2018.05.003
2. Anshory, I., Robandi, I., & Ohki, M. (2019). System Indentification of BLDC Motor and Optimization Speed Control Using Artificial Intelligent. International Journal of Civil Engineering and Technology (IJCIET), 10(07), 1–13.
3. Akhtar, M. A., & Saha, S. (2018). dSPACE Based Motor Testing Platform for Characterization of BLDC Motor Performance Under Different Loading Conditions. 2018 8th IEEE India International Conference on Power Electronics (IICPE), 1–6. https://doi.org/10.1109/IICPE.2018.8709566
4. Apatya, Y. B. A., Subiantoro, A., & Yusivar, F. (2017). Design and Prototyping of 3-Phase BLDC Motor. 2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering, 209–214. https://doi.org/10.1109/QIR.2017.8168483
5. Tutaj, A., Drabek, T., Dziwinski, T., Baranowski, J., & Piatek, P. (2018). Unintended synchronisation between rotational speed and PWM frequency in a PM BLDC drive unit. 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR), 959–964.
6. Karthikeyan, B., Ragavan, D., Maheshwaran, M., & Priya, R. B. (2018). Fuel Cell Fed BLDC Motor Drive. Journal of Science and Technology (JST), 3(3), 35–43.
7. Poompavai, T., & Kowsalya, M. (2019). Control and energy management strategies applied for solar photovoltaic and wind energy fed water pumping system : A review. Renewable and Sustainable Energy Reviews, 107, 108–122. https://doi.org/10.1016/j.rser.2019.02.023
8. Venu, G., & Kalyani, S. T. (2018). Design of FOPI Controller for Speed Control of BLDC Motor. International Journal of Pure and Applied Mathematics, 120(6), 645–662.
9. Kumari, S., & Verma, V. K. (2018). GA Based Design of Current Conveyor PID Controller for the Speed Control of BLDC Motor. International Conference on “Computational Intntelligence and Communication Technology” (CICT 2018) International Conference on “Computational Intelligence and Communication Technology,” 1–3. https://doi.org/10.1109/CIACT.2018.8480149
10. Zhang, S., & Zhuan, X. (2019). Study on adaptive cruise control strategy for battery electric vehicle. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/7971594
11. Latif, A., Arfianto, A. Z., Widodo, H. A., Rahim, R., & T.Helmy, E. (2020). Motor DC PID System Regulator for Mini Conveyor Drive Based-on Matlab. Journal of Robotics and Control (JRC), 1(6), 185–190. https://doi.org/10.18196/jrc.1636
12. Ahmed, A. H., El, A., Kotb, S. B., Ali, A. M., & Ahmed, A. H. (2018). Comparison between Fuzzy Logic and PI Control for the Speed of BLDC Motor. International Journal of Power Electronics and Drive System (IJPEDS), 9(3), 11591. https://doi.org/10.11591/ijpeds.v9.i3.pp1116-1123
13. Zhang, D., & Wang, J. (2019). Fuzzy PID Speed Control of BLDC Motor based on Model Design. Journal of Physics: Conference Series, 1303, 1–6. https://doi.org/10.1088/1742-6596/1303/1/012124
14. Avian, C., Sujanarko, B., & Kaloko, B. S. (2019). Response Improvement of BLDC Motor Speed using Extreme Learning Machine Controller. International Journal of Engineering Research And Management, 06(11), 1–7.
15. Kavathe, R., Chandle, J. O., Patil, N., & Kokare, M. (2018). ANFIS Based Speed Control of BLDC Motor with Bidirectional DC-DC Converter. International Journal of Research and Scientific Innovation (IJRSI), V(Vi), 153–158.
16. Hassan, A. K., Elksasy, M. S., Saraya, M. S., & Areed, F. F. (2018). Brushless DC Motor Speed Control using PID Controller, Fuzzy Controller, and Neuro-Fuzzy Controller. International Journal of Computer Applications, 180(30), 47–52. https://doi.org/10.5120/ijca2018916783
17. Ch, L., & Palakeerthi, R. (2015). BLDC Drive Control using Artificial Intelligence Technique. International Journal of Computer Applications, 118(4), 5–9. https://doi.org/10.5120/20731-3100
18. Gobinath, S., & Madheswaran, M. (2020). Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor. Soft Computing, 24(13), 10161–10180. https://doi.org/10.1007/s00500-019-04532-z
19. Krishna Veni, K. S., Senthil Kumar, N., & Senthil Kumar, C. (2019). A comparative study of universal fuzzy logic and PI speed controllers for four switch BLDC motor drive. International Journal of Power Electronics, 10(1–2), 18–32. https://doi.org/10.1504/IJPELEC.2019.096805
20. Varshney, A., Gupta, D., & Dwivedi, B. (2017). Speed response of brushless DC motor using fuzzy PID controller under varying load condition. Journal of Electrical Systems and Information Technology, 4(2), 310–321. https://doi.org/10.1016/j.jesit.2016.12.014
21. Zhang, D., & Wang, J. (2019). Fuzzy PID speed control of BLDC motor based on model design. Journal of Physics: Conference Series, 1303(1). https://doi.org/10.1088/1742-6596/1303/1/012124
22. Sheng, Y., Wang, X., Wang, L., & Hou, P. (2017). Fuzzy-PID control system design of brushless DC motor based on vector control. Proceedings - 2017 Chinese Automation Congress, CAC 2017, 2017-Janua, 5583–5587. https://doi.org/10.1109/CAC.2017.8243777
23. Kristiyono, R., & Wiyono, W. (2021). Autotuning Fuzzy PID Controller for Speed Control of BLDC Motor. Journal of Robotics and Control (JRC), 2(5). https://doi.org/10.18196/jrc.25114
24. Ghany, M. A. A., Shamsledin, M. A., & Ghany, A. M. A. (2017). A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor. 2017 Nineteenth International Middle East Power Systems Conference (MEPCOM), 1453–1458. https://doi.org/10.1109/MEPCON.2017.8301374
25. Ramya, A., Balaji, M., & Kamaraj, V. (2019). Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique. The Journal of Engineering, 2019(17), 3947–3950. https://doi.org/10.1049/joe.2018.8179
26. Özdemir, M. T., & Öztürk, D. (2017). Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies, 10(12). https://doi.org/10.3390/en10122134
27. Marzaki, M. H., Tajjudin, M., Rahiman, M. H. F., & Adnan, R. (2015). Performance of FOPI with error filter based on controllers performance criterion (ISE, IAE and ITAE). 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015, 5–10. https://doi.org/10.1109/ASCC.2015.7244851