Design of Extended Kalman Filter Speed Estimator and Single Neuron-Fuzzy Speed Controller for Sensorless Brushless DC Motor

Authors

  • Muhammad Rif’an Universitas Negeri Jakarta
  • Feri Yusivar Universitas Indonesia
  • Benyamin Kusumoputro Universitas Indonesia

Keywords:

BLDC, Extended Kalman Filter, Sensorless, Single Neuron-Fuzzy,

Abstract

Methods of estimation and control of BLDC presented in this paper. Because BLDCM is a motor without a brush then BLDC requires the sensor position to rotate the rotor and this is a weakness of the BLDC. A sensorless algorithm of Extended Kalman Filter (EKF) was proposed to cover this weakness. Additionally, BLDC is also a non-linear system. Thus, it is difficult to obtain accurate and good value PID parameter controller with a conventional PID method. In this paper, a single neural network - Fuzzy PID for BLDC developed. The experimental results show that the EKF is able to estimate the speed of the BLDC and single neural networks - Fuzzy PID controller makes BLDC system faster.

References

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Published

2018-02-05

How to Cite

Rif’an, M., Yusivar, F., & Kusumoputro, B. (2018). Design of Extended Kalman Filter Speed Estimator and Single Neuron-Fuzzy Speed Controller for Sensorless Brushless DC Motor. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-5), 157–161. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3648

Issue

Vol. 10 No. 1-5: Breakthrough To Excellence in Communication and Computer Engineering II

Section

Articles

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