Skip to main content
SpringerLink
Log in

The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, and motor power). The rotor speed is defined as the output parameter to determine the dynamic response, and 600 parametric data are obtained according to the simulation studies. In order to estimate the rotor speed of the BLDC motor modeled using artificial intelligence (AI), an advanced recurrent neural network architecture known as bidirectional long short-term memory has been designed. Rotor speed is successfully estimated with the proposed architecture, and as a result, the mean absolute percentage error value is calculated as 3.25%. These results show that the analysis of BLDC motor parameters can be determined quickly with the proposed AI method without long-running simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Kandiban, R.; Arulmozhiyal, R.: Design of adaptive fuzzy PID controller for speed control of BLDC motor. Int. J. Soft Comput. Eng. 2, 386–391 (2012)

    Google Scholar 

  2. Gouda, G.E.; Jyothi, N.: Analysis and co-simulation of BLDC motor drive with fault detection by FEA method. Int. J. Sci. Res. Dev. 5, 199–202 (2017)

    Google Scholar 

  3. Govindaraj, D.T.; Vishnu, S.: Simulation modelling of sensor less speed control of BLDC motor using artificial neural network. Int. J. Emerg. Trends Electr. Electron. 10, 7–15 (2014)

    Google Scholar 

  4. Leena, N.; Shanmugasundaram, R.: Artificial neural network controller for improved performance of brushless DC motor. In: 2014 International Conference on Power Signals Control and Computations (EPSCICON), pp. 1–6. IEEE (2014)

  5. Tipsuwanporn, V.; Piyarat, W.; Tarasantisuk, C., Identification and control of brushless DC motors using on-line trained artificial neural networks. In: Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No. 02TH8579), pp. 1290–1294. IEEE (2002).

  6. Solanki, S.: Brushless DC motor drive during speed regulation with artificial neural network controller. Int. J. Eng. Res. Appl. 6, 01–05 (2016)

    Google Scholar 

  7. Rubaai, A.; Ricketts, D.; Kankam, M.D.: Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives. IEEE Trans. Ind. Appl. 38, 441–447 (2002)

    Article  Google Scholar 

  8. Ganesh, C.; Prabhu, M.; Rajalakshmi, M.; Sumathi, G.; Bhola, V.; Patnaik, S.: ANN based PID controlled brushless DC drive system. In: Proceedings of the International Conference. on Advances in Electrical &\quad Electronics (2011)

  9. Mamadapur, A.; Mahadev, G.U.: Speed control of BLDC motor using neural network controller and PID controller. In: 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC), pp. 146–151. IEEE (2019).

  10. Belov, M.P.; Khoa, T.D.; Truong, D.D.: BLDC of robotic manipulators with neural torque compensator based optimal robust control. In: 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 437–441. IEEE (2019)

  11. Utomo, D.S.B.; Rizal, A.; Gaffar, A.F.O.: Model reference neural adaptive control based BLDC motor speed control. In: 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) , pp. 49–54. IEEE (2017)

  12. Singh, P.; Rai, P.: An ANN based X-PC target controller for speed control of permanent magnet brushless DC motor. In: Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005, pp. 1027–1032. IEEE (2005)

  13. Xia, C.-L.; Chen, W.: Sensorless control of brushless DC motors at low speed using neural networks. In: 2005 International Conference on Machine Learning and Cybernetics, pp. 1099–1103. IEEE (2005).

  14. Yi, Y.; Vilathgamuwa, D.M.; Rahman, M.A.: Implementation of an artificial-neural-network-based real-time adaptive controller for an interior permanent-magnet motor drive. IEEE Trans. Ind. Appl. 39, 96–104 (2003)

    Article  Google Scholar 

  15. Anshory, I.; Robandi, I.: Monitoring and optimization of speed settings for Brushless Direct Current (BLDC) using particle swarm optimization (PSO). In: 2016 IEEE Region 10 Symposium (TENSYMP), pp. 243–248. IEEE (2016)

  16. Sabanci, K.: Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation. Measurement 155, 107553 (2020)

    Article  Google Scholar 

  17. Toha, S.F.; Tokhi, M.O.: MLP and Elman recurrent neural network modelling for the TRMS. In: 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, pp. 1–6. IEEE (2008).

  18. Zhang, W.; Zhang, Q.; Xie, Y.; Zhang, J.: LSTM-based pitch range estimation from spectral information of brief speech input. In: 2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP), pp. 349–353. IEEE (2018)

  19. Akkaya, R.; Kulaksız, A.; Aydoğdu, Ö.: DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive. Energy Convers. Manag. 48, 210–218 (2007)

    Article  Google Scholar 

  20. Kumar, R.; Gupta, R.; Bhangale, S.; Gothwal, H.: ANN based control and estimation of direct torque controlled induction motor drive. Asian Power Electron. J. 2, 115–122 (2008)

    Google Scholar 

  21. Nekoubin, A.: Design a single-phase BLDC motor and finite-element analysis of stator slots structure effects on the efficiency. Int. J. Electr. Comput. Eng. 5, 685–692 (2011)

    Google Scholar 

  22. Yu, R.; Gao, J.; Yu, M.; Lu, W.; Xu, T.; Zhao, M.; Zhang, J.; Zhang, R.; Zhang, Z.: LSTM-EFG for wind power forecasting based on sequential correlation features. Futur. Gener. Comput. Syst. 93, 33–42 (2019)

    Article  Google Scholar 

  23. Kim, J.-G.; Lee, B.: Appliance classification by power signal analysis based on multi-feature combination multi-layer LSTM. Energies 12, 2804 (2019)

    Article  Google Scholar 

  24. Bengio, Y.; Simard, P.; Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5, 157–166 (1994)

    Article  Google Scholar 

  25. Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  26. Gers, F.A.; Schmidhuber, J.; Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451–2471 (2000)

    Article  Google Scholar 

  27. Gers, F.A.; Schraudolph, N.N.; Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)

    MathSciNet  MATH  Google Scholar 

  28. Chen, G.: A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint arXiv:0258 (2016)

  29. Zhao, L.; Wang, Q.; Jin, B.; Ye, C.: Short-term traffic flow intensity prediction based on CHS-LSTM. Arabian J. Sci. Eng. 45, 10845–10857 (2020)

    Article  Google Scholar 

  30. Mehrani, M.; Attarzadeh, I.; Hosseinzadeh, M.: Sampling rate prediction of biosensors in wireless body area networks using deep-learning methods. In: Simulation Modelling Practice and Theory, p. 102101 (2020)

  31. Liu, G.; Guo, J.: Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337, 325–338 (2019)

    Article  Google Scholar 

  32. Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  33. Warde-Farley, D.; Goodfellow, I.J.; Courville, A.; Bengio, Y.: An empirical analysis of dropout in piecewise linear networks, arXiv preprint arXiv: 0258 (2013)

  34. Kingma, D.P.; Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014)

  35. Feng, S.; Zhou, H.; Dong, H.: Using deep neural network with small dataset to predict material defects. Mater. Des. 162, 300–310 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Necmettin Erbakan University, 42090, Konya, Turkey

    Muhammed Fahri Unlersen

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Karamanoglu Mehmetbey University, 70200, Karaman, Turkey

    Selami Balci, Muhammet Fatih Aslan & Kadir Sabanci

Authors
  1. Muhammed Fahri Unlersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selami Balci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammet Fatih Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kadir Sabanci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kadir Sabanci.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Unlersen, M.F., Balci, S., Aslan, M.F. et al. The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications. Arab J Sci Eng 47, 2639–2648 (2022). https://doi.org/10.1007/s13369-021-05700-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05700-w

Keywords

Search

Navigation