PERFORMANCE IMPROVEMENT ON MOTOR BLDC SPEED CONTROLLER BY USING MULTI CONTROLLER WITH SUMMATION TECHNIQUE
Keywords:
ANN, BLDC motor, fuzzy, PID, summation speed controller
Abstract
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.
References
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2. 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
3. Bulgakov, A. G., & Kruglova, T. N. (2017). Intelligent method of electric drive diagnostic with due account for its operation mode. Journal of Applied Engineering Science, 15(4), 426–432. https://doi.org/10.5937/jaes15-14651
4. 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.
5. 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
6. 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
7. 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.
8. Sundaram, A., & Ramesh, G. P. (2017). Sensor less Control of BLDC Motor using Fuzzy logic controller for Solar power Generation. International Journal of MC Square Scientific Research, 9(2), 70–79.
9. 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
10. Boubiche, D. E., Pathan, A. S. K., Lloret, J., Zhou, H., Hong, S., Amin, S. O., & Feki, M. A. (2018). Advanced Industrial Wireless Sensor Networks and Intelligent IoT. IEEE Communications Magazine, 56(2), 14–15. https://doi.org/10.1109/MCOM.2018.8291108
11. 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.
12. Kumar, R., & Singh, B. (2016). BLDC Motor Driven Solar PV Array Fed Water Pumping System Employing Zeta Converter. 2014 IEEE 6th India International Conference on Power Electronics (IICPE). https://doi.org/10.1109/TIA.2016.2522943
13. Gurumurthy, S. R., Agarwal, V., & Sharma, A. (2016). A Novel Dual Winding BLDC Generator-Buck Converter Combination for Enhancement of the Harvested Energy from a Flywheel. IEEE Transactions on Industrial Electronics, 63(12), 7563–7573. https://doi.org/10.1109/TIE.2016.2594163
14. 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.
15. Prasad, A., & Nair, U. (2017). An Intelligent Fuzzy Sliding Mode Controller for a BLDC Motor. International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2017), (Icimia), 274–278. https://doi.org/10.1109/ICIMIA.2017.7975618
16. 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
17. Lad, C. K., & Chudamani, R. (2017). A simple overlap angle control strategy for reducing commutation torque ripple in a brushless DC motor drive. Engineering Science and Technology, an International Journal, 20(4), 1406–1419. https://doi.org/10.1016/j.jestch.2017.09.005
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19. 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
20. 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
21. 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
22. 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.
23. 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.
24. Mitra, S., & Ojha, A. (2019). Performance Analysis of BLDC Motor Drive Using PI and Fuzzy Logic Control Scheme. International Research Journal of Engineering and Technology(IRJET), 2(6), 916–922.
25. Ahmed, A. H., Kotb, A. E. S. B., & Ali, A. M. (2018). Comparison between fuzzy logic and PI control for the speed of BLDC motor. International Journal of Power Electronics and Drive Systems, 9(3), 1116–1123. https://doi.org/10.11591/ijpeds.v9n3.pp1116-1123
26. 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
27. 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
28. Yazdanpanah, R., & Soltani, J. (2007). Robust backstepping control of induction motor drives using artificial neural networks and sliding-mode flux observers. International Journal of Engineering, Transactions A: Basics, 20(3), 221–232.
29. 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
30. 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
31. 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
32. 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
