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Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control

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International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

The magnetic levitation system (MLS) is a versatile technology with wide-ranging applications, offering benefits like contactless operation, high precision, energy efficiency, and reduced maintenance. However, achieving precise and stable levitation, especially during transient states, remains a critical challenge. This paper presents a novel method to improve the transient levitation response of MLS by integrating fuzzy logic with artificial neural network (ANN) control. By leveraging the strengths of both methodologies, the proposed hybrid control aims to improve the performance of MLS during transient operations such as initial levitation. The hybrid control consists of conventional control (PID position and PI current controls), off-line ANN identification, ANN control and fuzzy logic. Experimental comparisons with PID and disturbance observer show that the proposed hybrid ANN control improves not only transient response during the initial levitation (the rise and settling times by 92.7% and 85.0%, respectively) but also the sinusoidal command following by 57.8%. The performance improvement and stability of the proposed control were discussed by measuring the closed-loop frequency responses.

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Abbreviations

c i :

Leakage Current of the Electromagnet

d :

Disturbance

E, ER :

Fuzzy Sets for e and er

E :

Position Error of the Iron Ball

Er :

Position Error Rate of the Iron Ball

\({F}_{em}\) :

Electromagnetic Force

\({F}_{emP1}\) :

Coil Inductance

\({F}_{emP2}\) :

Coefficients of Electromagnetic Force

f i :

Time Constant of the Electromagnet

g :

Gravity Acceleration

k i :

Voltage-Current Static Gain

m :

Mass of the Steel Ball

P :

Fuzzy set for output variable

p :

Fuzzy Compensation

r :

Reference Command

u :

Control Effort (Voltage)

x 1, x 2, x 3, x 0 :

State (The Position of the Iron Ball, Velocity of the Iron Ball, Current, Equilibrium State)

y :

Output

References

  1. Maslen, E. H., Schweitzer, G. & Bleuler, H., (2009). “Magnetic Bearings: Theory, Design, and Application to Rotating,” Springer,

  2. Zhou, L., & Wu, J. (2022). Magnetic levitation technology for precision motion systems: a review and future perspectives. International Journal of Automation Technology, 16(4), 386–402.

    Article  Google Scholar 

  3. Yoo, S. J., Kim, S., Cho, K. H., & Ahn, H. J. (2021). Data-driven self-sensing technique for active magnetic bearing. International Journal of Precision Engineering and Manufacturing, 22(6), 1031–1038.

    Article  Google Scholar 

  4. Pandey, A., & Adhyaru, D. M. (2023). Control techniques for electromagnetic levitation system: A literature review. International Journal of Dynamics and Control, 11(2), 441–451.

    Article  Google Scholar 

  5. Li, F., Sun, Y., Xu, J., He, Z., & Lin, G. (2023). Control Methods for Levitation System of EMS-Type Maglev Vehicles: An Overview. Energies, 16(7), 2995.

    Article  Google Scholar 

  6. Yang, Z. J., Miyazaki, K., Kanae, S., & Wada, K. (2004). Robust position control of a magnetic levitation system via dynamic surface control technique. IEEE Transactions on Industrial Electronics, 51(1), 26–34.

    Article  Google Scholar 

  7. San, S. T., Yun, J., & Kim, D. (2023). Buoy-Inspired Hybridized Energy Harvester with Freestanding Dielectric Oscillator Towards Sustainable Blue Energy Harvesting. International Journal of Precision Engineering and Manufacturing-Green Technology, 10(3), 757–771.

    Article  Google Scholar 

  8. Jiang, Y., Yang, C., & Ma, H. (2016). A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2016/7217364

    Article  Google Scholar 

  9. Lim, J., Lee, S., Noh, J., Lee, W., Su, P. C., & Yoon, Y. J. (2023). Effectiveness of Mental Health Care by Using Machine Learning on Manufacturing Worker. International Journal of Precision Engineering and Manufacturing-Smart Technology, 1(2), 227–242.

    Article  Google Scholar 

  10. Nghi, H. V., Nhien, D. P., & Ba, D. X. (2022). A LQR neural network control approach for fast stabilizing rotary inverted pendulums. International Journal of Precision Engineering and Manufacturing, 23(1), 45–56.

    Article  Google Scholar 

  11. Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23(1), 111–129. https://doi.org/10.1007/s12541-021-00600-3

    Article  Google Scholar 

  12. Lin, F.-J., Shieh, H.-J., Teng, L.-T., & Shieh, P.-H. (2005). Hybrid Controller with Recurrent Neural Network for Magnetic Levitation System. IEEE Trans. on Magnetics, 41(7), 2260–2269.

    Article  Google Scholar 

  13. Qin, Y., Peng, H., Zhou, F., Zeng, X., & Wu, J. (2015). Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model. Journal of the Franklin Institute, 352(10), 4309–4338.

    Article  MathSciNet  Google Scholar 

  14. Wei, Z., Huang, Z., & Zhu, J. (2020). Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control. Mathematical Problems in Engineering. https://doi.org/10.1155/2020/8935423

    Article  Google Scholar 

  15. Yang, W., Meng, F., Meng, S., Man, S., & Pang, A. (2020). Tracking Control of Magnetic Levitation System Using Model-Free RBF Neural Network Design. IEEE Access, 8, 204563–204572.

    Article  Google Scholar 

  16. Silva, B. E., & Barboda, R. S. (2021). Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform. Applied Sciences, 11(6), 2535.

    Article  Google Scholar 

  17. Sun, Y., Xu, J., Lin, G., Ji, W., & Wang, L. (2022). RBF Neural Network-Based Supervisor Control for Maglev Vehicles on an Elastic Track with Network Time Delay. IEEE Trans. on Industrial Informatics, 18(1), 509–519.

    Article  Google Scholar 

  18. Tang, J., Huang, Z., Zhu, Y., & Zhu, J. (2022). Neural network compensation control of magnetic levitation ball position based on fuzzy inference. Scientific reports. https://doi.org/10.1038/s41598-022-05900-w

    Article  Google Scholar 

  19. Huang, Z., Zhu, J., Shao, J., Wei, Z., & Tang, J. (2022). Recurrent neural network based high-precision position compensation control of magnetic levitation system. Scientific reports, 12, 1795.

    Article  Google Scholar 

  20. INTECO, MLS (Magnetic Levitation Systems), https://www.inteco.com.pl/products/magnetic-levitation-systems/

  21. Zheng, Y. P., & Ahn, H.-J. (2024). Control Boost of a Magnetic Levitation System with Disturbance Observers. Journal of the Korean Society for Precision Engineering, 41(4), 273–278.

    Article  Google Scholar 

  22. Maglev Modeling, https://mathworks.com/help/deeplearning/ug/maglev-modeling.html

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Acknowledgements

This work was supported by the Energy technology R&D program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (RS-2023-00242282).

Funding

Korea Institute of Energy Technology Evaluation and Planning,RS-2023-00242282,Hyeong-Joon Ahn.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Graduate School, Soongsil University, Seoul, 06978, Republic of Korea

    Yupeng Zheng

  2. School of Mechanical Engineering, Soongsil University, Seoul, 06978, Republic of Korea

    Hyeong-Joon Ahn

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  1. Yupeng Zheng
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Correspondence to Hyeong-Joon Ahn.

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Zheng, Y., Ahn, HJ. Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control. Int. J. Precis. Eng. Manuf. (2024). https://doi.org/10.1007/s12541-024-01173-7

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  • DOI: https://doi.org/10.1007/s12541-024-01173-7

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