Estimasi Kecepatan Motor Brushless DC dengan Menggunakan Metode Sliding Mode Observer

Rizqy Abdurrahman, Novie Ayub Windarko, Bambang Sumantri

Abstract


Pada dasarnya motor brusless DC  (BLDC) atau yang biasa juga disebut permanent magnet synchronous motor (PMSM) menggunakan hall-sensor untuk mengetahui posisi dan kecepatan dari motor tersebut. Data nilai arus (I) dan tegangan (V) pada pemodelan dasar dari motor BLDC sebagai masukan dari metode sliding mode observer (SMO). Metode sensorless yang didasarkan pada SMO diajukan untuk menggantikan perangkat hall-sensor untuk mengestimasi posisi rotor dan kecepatan motor BLDC. Pengujian akan dilakukan menggunakan aplikasi power simulator (PSim). Untuk mendapatkan error estimasi kecepatan pengujian dilakukan dengan membandingkan kecepatan aktual dengan kecepatan estimasi. Pengujian dilakukan dengan dua (2) nilai kecepatan yang berbeda yaitu sebesar 1000 r/min dan 1200 r/min dan dua (2) beban mekanik yang berbeda yaitu sebesar 0.1 Nm dan 0.5 Nm. Hasil dari simulasi yang telah dilakukan dengan kecepatan motor BLDC sebesar 1000 r/min dan beban mekanik sebesar 0.1 Nm, didapatkan nilai error estimasi kecepatan sebesar 6,7%, dengan kecepatan sebesar 1000 r/min dan beban sebesar 0.5 Nm, didapatkan nilai error estimasi sebesar 7,2%, dengan kecepatan motor sebesar 1200 r/min dan beban sebesar 0.1 Nm, didapatkan nilai error estimasi sebesar 9,5%, dengan kecepatan motor sebesar 1200 r/min dan beban sebesar 0.5 Nm, didapatkan nilai error estimasi sebesar 9,8%. Dari pengujian tersebut membuktikan sliding mode observer dapat bekerja dengan baik karena nilai error estimasi kurang dari 10% dan merupakan metode yang robust.

Keywords


BLDC, Motor, SMO, PSIm

References


Aboutanios, E. (2017). An adaptive clarke transform based estimator for the frequency of balanced and unbalanced three-phase power systems. 25th European Signal Processing Conference, EUSIPCO 2017, 2017-Janua(1), 1001–1005. https://doi.org/10.23919/EUSIPCO.2017.8081358

Bondre, V. S., & Thosar, A. G. (2017). Mathematical modeling of direct torque control of BLDC motor. 2017 International Conference on Innovative Research in Electrical Sciences, IICIRES 2017. https://doi.org/10.1109/IICIRES.2017.8078304

Chang, P. I. T., Lin, X. Y., & Yu, I. J. (2019). Sensorless BLDC motor sliding mode controller design for interference recovery. 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, 1780–1785. https://doi.org/10.1109/CoDIT.2019.8820383

Chen, X., & Liu, G. (2020). Sensorless optimal commutation steady speed control method for a nonideal back-EMF BLDC motor drive system including buck converter. IEEE Transactions on Industrial Electronics, 67(7), 6147–6157. https://doi.org/10.1109/TIE.2019.2945282

Gan, M. G., Zhang, M., Zheng, C. Y., & Chen, J. (2018). An adaptive sliding mode observer over wide speed range for sensorless control of a brushless DC motor. Control Engineering Practice, 77(May), 52–62. https://doi.org/10.1016/j.conengprac.2018.05.004

Geraee, S., Shafiei, M., Sahami, A. R., & Alavi, S. (2017). Position sensorless and adaptive speed design for controlling brushless DC motor drives. 2017 North American Power Symposium, NAPS 2017. https://doi.org/10.1109/NAPS.2017.8107246

Liang, D., Li, J., & Qu, R. (2017). Sensorless Control of Permanent Magnet Synchronous Machine Based on Second-Order Sliding-Mode Observer With Online Resistance Estimation. IEEE Transactions on Industry Applications, 53(4), 3672–3682. https://doi.org/10.1109/TIA.2017.2690218

Liu, S., Qiu, Z., & Chen, W. (2019). Sensorless Control with Sliding Mode Observer for a Brushless DC Motor based on Concave Function. Proceedings of 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019, (Imcec), 872–876. https://doi.org/10.1109/IMCEC46724.2019.8984058

Poovizhi, M., Senthil Kumaran, M., Ragul, P., Irene Priyadarshini, L., & Logambal, R. (2017). Investigation of mathematical modelling of brushless dc motor(BLDC) drives by using MATLAB-SIMULINK. International Conference on Power and Embedded Drive Control, ICPEDC 2017, 178–183. https://doi.org/10.1109/ICPEDC.2017.8081083

Putra, E. H., Has, Z., & Effendy, M. (2018). Robust adaptive sliding mode control design with genetic algorithm for Brushless DC motor. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018-Octob, 330–335. https://doi.org/10.1109/EECSI.2018.8752768

Rif’an, M., Yusivar, F., & Kusumoputro, B. (2019). Sensorless-BLDC motor speed control with ensemble Kalman filter and neural network. Journal of Mechatronics, Electrical Power, and Vehicular Technology, 10(1), 1. https://doi.org/10.14203/j.mev.2019.v10.1-6

Syamsiana, I. N., & Wang, M. (2019). A Study of Sliding Mode Observer Sensorless of Brushless Motor using Embedded Coder Matlab/Simulink. The 6th International Conference on Electrical, Electronics and Information Engineering (ICEEIE 2019), (1).

Topal, M., Iskender, I., & Genc, N. (2019). Sensorless speed control of a BLDC motor using improved sliding mode observer technique. International Journal on Technical and Physical Problems of Engineering, 11(1), 1–9.

Venkateswari, K. (2020). A sensor less BLDC motor drive using sliding mode observer for electric vehicle. Malaya Journal of Matematik, (2), 3544–3548.

Zaky, M. S., Metwaly, M. K., Azazi, H. Z., & Deraz, S. A. (2018). A New Adaptive SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Very Low Frequencies. IEEE Transactions on Industrial Electronics, 65(9), 6901–6911. https://doi.org/10.1109/TIE.2018.2793206




DOI: http://dx.doi.org/10.28926/briliant.v6i3.700

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