Skip to main content
SpringerLink
Log in

Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In brushless direct current (BLDC) motors, speed control is a prominent operation based on which these motors are widely used in higher-end industrial applications including robotics, aeronautics, disk drives, factory automation, consumer electronics, transport and military applications. A novel wavelet neural learning (WNL)-based type-2 fuzzy system controller is devised in this paper to accomplish effective speed control of the BLDC motor with the set specifications. The performance characteristics of the BLDC motor are simulated and analysed based on the wavelet neural learning-based type-2 fuzzy proportional–integral–derivative (PID) controller. This work employs wavelet neural learning to provide inputs to the constructed new type-2 fuzzy PID controller model, and this drives the BLDC motor mechanism. The gain values of the PID controller are tuned with the developed WNL-based type-2 fuzzy system to carry out most operational speed regulation process for the motor mechanism. The developed speed controller model for the BLDC motor is tested for its superiority by performing step variations of the input signal and also for load disturbances. In WNL, to derive the output a wavelet function is applied and this drives the operation of the type-2 fuzzy system PID controller and attains better gain values for speed control operation. The classic slime mould algorithm is used in this contribution to tune the weight coefficients during wavelet neural learning process and due to which the delayed convergence is controlled. Results attained during the simulation process prove the effectiveness and superiority of the proposed speed controller for the design specifications of the BLDC motor in comparison with the earlier literature contributions made.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Niapour SKM, Danyali S, Sharifian MBB, Feyzi MR (2011) Brushless DC motor drives supplied by PV power system based on Z-source inverter and FL-IC MPPT controller. Energy Convers Manag 52(8–9):3043–3059

    Article  Google Scholar 

  2. Shanmugasundram R, Zakariah KM, Yadaiah N (2012) Implementation and performance analysis of digital controllers for brushless DC motor drives. IEEE/ASME Trans Mechatron 19(1):213–224

    Article  Google Scholar 

  3. Jin-feng X, Lei Z, Min O, Fei-Hui Z (2012) BLDC motor field orientation control system based on LPIDBP neural network. IET Int Conf Inf Sci Control Eng 2012:379–379. https://doi.org/10.1049/cp.2012.2476

    Article  Google Scholar 

  4. Kandiban R, Arulmozhiyal R (2012) Speed control of BLDC motor using adaptive fuzzy PID controller. Procedia Eng 38:306–313

    Article  Google Scholar 

  5. Nizam M, Mujianto A, Triwaloyo H (2013) Modelling on BLDC motor performance using artificial neural network (ANN). In: 2013 joint international conference on rural information & communication technology and electric-vehicle technology (rICT & ICeV-T) (pp. 1–4). IEEE

  6. Rubaai A, Jerry J (2013) Hybrid fuzzy Bang–Bang mode controller with switching function for electric motor drive applications. IEEE Trans Ind Appl 50(3):2269–2276

    Article  Google Scholar 

  7. Premkumar K, Manikandan BV (2014) Adaptive neuro-fuzzy inference system based speed controller for brushless DC motor. Neurocomputing 138:260–270

    Article  Google Scholar 

  8. Daya JF, Sanjeevikumar P, Blaabjerg F, Wheeler PW, Ojo JO (2015) Implementation of wavelet-based robust differential control for electric vehicle application. IEEE Trans Power Electron 30(12):6510–6513

    Article  Google Scholar 

  9. Premkumar K, Manikandan BV (2015) Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 157:76–90

    Article  Google Scholar 

  10. Kumpanya D, Thaiparnat S, Puangdownreong D (2015) Parameter identification of BLDC motor model via metaheuristic optimization techniques. Procedia Manuf 4:322–327

    Article  Google Scholar 

  11. Prabu MJ, Poongodi P, Premkumar K (2016) Fuzzy supervised online coactive neuro-fuzzy inference system-based rotor position control of brushless DC motor. IET Power Electron 9(11):2229–2239

    Article  Google Scholar 

  12. Premkumar K, Manikandan BV (2016) Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Eng Sci Technol Int J 19(2):818–840

    Google Scholar 

  13. Tariq M, Bhattacharya TK, Varshney N, Rajapan D (2016) Fast response Antiwindup PI speed controller of Brushless DC motor drive: modeling, simulation and implementation on DSP. J Electr Syst Inf Technol 3(1):1–13

    Article  Google Scholar 

  14. Gowthaman E, Vinodhini V, Hussain MY, Dhinakaran SK, Sabarinathan T (2017) Speed control of permanent magnet brushless DC motor using hybrid fuzzy proportional plus integral plus derivative controller. Energy Procedia 117:1101–1108

    Article  Google Scholar 

  15. Varshney A, Gupta D, Dwivedi B (2017) Speed response of brushless DC motor using fuzzy PID controller under varying load condition. J Electr Syst Inf Technol 4(2):310–321

    Article  Google Scholar 

  16. Joy J, Ushakumari S (2017) Performance comparison of a Canonical switching Cell with SPWM and SVPWM fed sensorless PMBLDC motor drive under conventional and fuzzy logic controllers. J Frank Inst 354(14):5996–6032

    Article  MathSciNet  Google Scholar 

  17. Peng X, Jia M, He L, Yu X, Lv Y (2018) Fuzzy sliding mode control based on longitudinal force estimation for electro-mechanical braking systems using BLDC motor. CES Trans Electr Mach Syst 2(1):142–151

    Article  Google Scholar 

  18. Rahmani O, Iltarabian M, Sadrossadat SA (2018) Modeling and simulation of speed and efficiency of BLDC motor as a starter motor based on multilayer perceptron (MLP) neural network. In: 2018 IEEE transportation electrification conference and Expo, Asia-Pacific (ITEC Asia-Pacific) (pp. 1–5). IEEE.

  19. Maharajan MP, Xavier SAE (2018) Design of speed control and reduction of torque ripple factor in BLDC motor using spider based controller. IEEE Trans Power Electron 34(8):7826–7837

    Article  Google Scholar 

  20. El-Samahy AA, Shamseldin MA (2018) Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control. Ain Shams Eng J 9(3):341–352

    Article  Google Scholar 

  21. Goswami R, Joshi D (2018) Performance review of fuzzy logic based controllers employed in brushless DC motor. Procedia Comput Sci 132:623–631

    Article  Google Scholar 

  22. Potnuru D, Ch S (2018) Design and implementation methodology for rapid control prototyping of closed loop speed control for BLDC motor. J Electr Syst Inf Technol 5(1):99–111

    Article  Google Scholar 

  23. Potnuru D, Mary KA, Babu CS (2019) Experimental implementation of flower pollination algorithm for speed controller of a BLDC motor. Ain Shams Eng J 10(2):287–295

    Article  Google Scholar 

  24. Sadrossadat SA, Rahmani O (2020) ANN-based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor. IET Electr Power Appl 14(6):951–960

    Article  Google Scholar 

  25. Shifat TA, Jang-Wook H (2020) Remaining useful life estimation of BLDC motor considering voltage degradation and attention-based neural network. IEEE Access 8:168414–168428

    Article  Google Scholar 

  26. Kommula BN, Kota VR (2020) Direct instantaneous torque control of Brushless DC motor using firefly Algorithm based fractional order PID controller. J King Saud Univ-Eng Sci 32(2):133–140

    Google Scholar 

  27. Chandra S, Gaur P, Pathak D (2020) Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system. Comput Electr Eng 86:106730

    Article  Google Scholar 

  28. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  29. Sreeram K (2018) Design of fuzzy logic controller for speed control of sensorless BLDC motor drive. In: 2018 international conference on control, power, communication and computing technologies (ICCPCCT) (pp. 18–24). IEEE. 21

  30. Kumar AS, Sami K (2018) Speed control of brushless DC motor using line to line back EMF by ANFIS controller. Indian J Public Health Res Dev 9(10):19

    Google Scholar 

  31. Premkumar K, Manikandan BV (2018) Stability and performance analysis of ANFIS tuned PID based speed controller for brushless DC motor. Curr Signal Trans Therapy 13(1):19–30

    Article  Google Scholar 

  32. Gobinath S, Madheswaran M (2020) Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor. Soft Comput 24:10161–10180. https://doi.org/10.1007/s00500-019-04532-z

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Pachal, Namakkal, Tamil Nadu, 637018, India

    Anand Karuppannan

  2. Centre for Research in Signal and Image Processing, Department of Electronics and Communication Engineering, Muthayammal Engineering College (Autonomous), Rasipuram, Tamil Nadu, 637408, India

    Madheswaran Muthusamy

Authors
  1. Anand Karuppannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madheswaran Muthusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madheswaran Muthusamy.

Ethics declarations

Conflict of interest

The authors confirm no conflict of interest in publishing this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karuppannan, A., Muthusamy, M. Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor. Neural Comput & Applic 33, 13481–13503 (2021). https://doi.org/10.1007/s00521-021-05971-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05971-2

Keywords

Search

Navigation