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An Improved Self-tuning Control Mechanism for BLDC Motor Using Grey Wolf Optimization Algorithm

  • Murali Muniraj
  • R. Arulmozhiyal
  • D. Kesavan
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  • 33 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 637)

Abstract

Brushless DC motor employed wide role actuator plays a significant role in many real-time applications. This paper investigates modelling and simulation of BLDC motor with an optimization algorithm for self-tuning parameters in an unknown alleyway. Grey wolf algorithm (GWA), an intelligent control algorithm is developed with the behaviour of a wolf while hunting the pathways. Further the algorithm also reduces noise cancellation that accurately reduces the impact on load during unknown alleyways. GWA is employed to acquire the gain values and self-tune parameters for the inverter to drive BLDC motor, and constant term is introduced to reduce overreach of motor speed and position of shaft. A comparative analysis is conducted among the feedback controller for BLDC motor optimization such as neuro-ANN and fuzzy-PID through simulation. Results suggest the proposed GWA algorithm holds better performance and reduce error in comparison with the other two optimization methods.

Keywords

BLDC motor Wolf Optimization Optimization Alleyway 

References

  1. 1.
    Djerioui, A., Houari, A., Ait-Ahmed, M., Benkhoris, M., Chouder, A., Machmoum, M.: Grey wolf based control for speed ripple reduction at low speed operation of PMSM drives. ISA Trans. 74, 111–119 (2018)CrossRefGoogle Scholar
  2. 2.
    Bianchini, C., Immovilli, F., Cocconcelli, M., Rubini, R., Bellini, A.: Fault detection of linear bearings in brushless AC linear motors by vibration analysis. IEEE Trans. Ind. Electron. 58(5), 1684–1694 (2011)CrossRefGoogle Scholar
  3. 3.
    Gao, Z.M., Zhao, J.: An improved grey wolf optimization algorithm with variable weights. Comput. Intell. Neurosci. 2019(Article ID 2981282), 13 (2019)Google Scholar
  4. 4.
    Jordehi, A.R.: Optimal scheduling of home appliances in home energy management systems using grey wolf optimisation (Gwo) algorithm. In: 2019 IEEE Milan Power Tech. Milan, Italy, pp. 1–6 (2019)Google Scholar
  5. 5.
    Sun, X., Hu, C., Lei, G., Guo, Y., Zhu, J.: State feedback control for a PM hub motor based on grey wolf optimization algorithm. IEEE Trans. Power Electron.  https://doi.org/10.1109/tpel.2019.2923726CrossRefGoogle Scholar
  6. 6.
    Jordehi, A.R.: Optimal scheduling of home appliances in home energy management systems using grey wolf optimisation (Gwo) algorithm. In: 2019 IEEE Milan Power Tech. Milan, Italy, pp. 1–6 (2019).  https://doi.org/10.1109/ptc.2019.8810406
  7. 7.
    Al Saaideh, M.I., Mazideh, B.B., Abu-Al-Nadi, D.I.: Grey wolf optimizer for optimal design of digital HR filter. In: 2019 10th International Conference on Information and Communication Systems (ICICS). Irbid, Jordan, pp. 256–261 (2019)Google Scholar
  8. 8.
    Murali, M., Arulmozhiyal, R.: Modeling, simulation of control actuation system with fuzzy-PID logic controlled brushless motor drives for missiles glider applications. Sci. World J. Hindawi Publishing Corporation 2015, ISSN: 1537–744X, 1–15 (2015)Google Scholar
  9. 9.
    Murali, M., Arulmozhiyal, R.: Intelligent optimum control of brushless DC motor. Int. J. Control. Theory Appl. Int. Sci. Press. ISSN: 09745572, 10(8), 49–58 (2017)Google Scholar
  10. 10.
    Sun, C., He, W., Ge, W., Chang, C.: Adaptive neural network control of biped robots. IEEE Trans. Syst. Man, Cybern. Syst. 47(2), 315–326 (2017)Google Scholar
  11. 11.
    Ma, H., et al.: Neural-network-based distributed adaptive robust control for a class of nonlinear multi-agent systems with time delays and external noises. IEEE Trans. Syst. Man, Cybern. Syst. 46(6), 750–758 (2016)CrossRefGoogle Scholar
  12. 12.
    Ramya, A., Balaji, M., Kamaraj, V.: Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique. J. Eng. 2019(17), 3947–3950 (2019)CrossRefGoogle Scholar
  13. 13.
    Verma, V., Chauhan, S.: Adaptive PID-fuzzy logic controller for brushless DC motor. In: 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). Coimbatore, India, pp. 445–449 (2019)Google Scholar
  14. 14.
    Peng, X., Jia, M., He, L., Yu, X., Lv, Y.: 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 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Murali Muniraj
    • 1
  • R. Arulmozhiyal
    • 1
  • D. Kesavan
    • 1
  1. 1.Department of Electrical and Electronics EngineeringSona College of TechnologySalemIndia

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