SLGP Header

Parameter Identification in BLDC Motor using Optimization Technique

jasem Front Page
Brushless DC (BLDC) motor widely utilized in industrial automation, aerospace, and military appliances. The accurate model and efficiency parameter depending upon its analysis and design of the BLDC motor. The parameter identification is derived by practical mathematical model via optimisation techniques. The two optimization methods for parameter identification in BLDC, i.e. Deep neural network (DNN) and BAT algorithm are employed. The DNN and BAT optimisation technique can provide optimal BLDC model parameters. The speed, temperature, current and voltage of BLDC motor are measured using concern sensors with Arduino controller and analysed.

Keywords:Arduino, BAT algorithm, BLDC, current, DNN, speed, temperature and voltage.


  1. Tingna Shi, Yuntao Guo, Peng Song, and Changliang Xia, A New Approach of Minimizing Commutation Torque Ripple for Brushless DC Motor Based on DC-DC Converter, IEEE Trans. Ind. Electron., 57 (10), 2010, 3483-3490.
  2. Sathyan A., Milivojevic N., Lee Y-J., Krishnamurthy M., and EmadiA., An FPGA-based novel digital PWM control scheme for BLDC motor drives, IEEE Trans. Ind. Electron., 56 (8), 2009, 3040-3049.
  3. Kanimozhi.G and Sreedevi.VT., Modeling and Control of Bridgeless Interleaved PFC boost converter, Transylvanian Review., 24 (11), 2016, 2915-2924.
  4. Majoros WH, Advanced Methods for Parameter Estimation, Online supplement to: Methods for Computational Gene Prediction, Cambridge University Press, 2007.
  5. Liu Y., Zhu Z. Q., and Howe D., Commutation torque ripple minimization in direct-torque-controlled PM brushless dc drives, IEEE Trans. Ind. Electron. 43 (4), 1012-1021.
  6. Rodriguez F., and Emadi A., A novel digital control technique for brushless dc motor drives, IEEE Trans. Ind. Electron., 54 (5), 2365-2373.