Estimation performance of the novel hybrid estimator based on machine learning and extended Kalman filter proposed for speed-sensorless direct torque control of brushless direct current motor

İNAN R., AKSOY B., Salman O. K. M.

Engineering Applications of Artificial Intelligence, vol.126, 2023 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 126
  • Publication Date: 2023
  • Doi Number: 10.1016/j.engappai.2023.107083
  • Journal Name: Engineering Applications of Artificial Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Artificial intelligence, BLDC motor, Extended Kalman filter, Machine learning
  • Isparta University of Applied Sciences Affiliated: Yes

Abstract

In this study, machine learning (ML) based methods are used to estimate rotor mechanical speed of brushless direct current (BLDC) motors. Training performances of approaches such as Artificial Neural Network, k-Nearest Neighbor, and Random Forest in the ML-based speed estimator are tested using the datas obtained from the direct torque control (DTC) drive system of BLDC motor in simulation and it is seen that the ANN approach has the highest accuracy. In addition, a novel extended Kalman filter (EKF)-based estimator is proposed for the estimation of back-EMFs of BLDC motor. A hybrid estimation method is proposed by using the developed ML-based speed estimator with the proposed EKF-based estimator and its estimation performance is tested in simulation on DTC drive system.