Design of extended kalman filter speed estimator and single neuron-fuzzy speed controller for sensorless brushless DC motor

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)157-161
Number of pages5
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume10
Issue number1-5
Publication statusPublished - 1 Jan 2018

Keywords

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

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