Skip to main content

Advertisement

Springer Nature Link
Log in

Deep learning controller design of embedded control system for maglev train via deep belief network algorithm

  • Published:
Design Automation for Embedded Systems Aims and scope Submit manuscript

Abstract

The maglev train has been successful in practice as a new type of ground transportation. Owing to the inherent nonlinearity and open-loop instability of the electromagnetic suspension (EMS) system, an analogue or a digital controller is used to control the maglev trains’ stability. With the rapid development of embedded systems and artificial intelligence, intelligent digital control has begun to replace the conventional analogue control technology creating a new approach to the EMS control system. This paper proposes a hardware module for an embedded levitation controller based on digital signal processor and field programmable gate array, hence producing an open loop mathematical model of the embedded maglev control system. The deep learning controller is then developed based on a deep belief network (DBN) algorithm and a proportional integral derivative feedback controller. The simulations are conducted in the MATLAB environment after training the DBN. Simulation results are compared with those obtained from the conventional controller. Finally, experiments are implemented to examine the feasibility in practice of the application of the DBN into a maglev embedded control system. The system, with the proposed controller, can accurately track the target airgap of 8 mm. The maximum tracking error of sinusoidal trajectory is 0.17 mm and the maximum tracking error of step trajectory is 0.98 mm. Both simulation and experimental results are included in this paper to show that the proposed deep learning controller can be more robust and less complicated to implement in maglev control applications.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Thornton RD (2009) Efficient and affordable maglev opportunities in the United States. Proc IEEE 97:1901–1921

    Google Scholar 

  2. Sun Y, Xu J, Qiang H, Chen C, Lin G (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141:217–226

    Google Scholar 

  3. Boldea I, Tutelea L, Xu W et al (2017) Linear electric machines, drives, and MAGLEVs: an overview. IEEE Trans Ind Electron 65(9):7504–7515

    Google Scholar 

  4. Sun Y, Xu J, Qiang H, Lin G (2019) Adaptive neural-fuzzy robust position control scheme for maglev train systems with experimental verification. IEEE Trans Ind Electron 66(11):8589–8599

    Google Scholar 

  5. Sun Y, Xu J, Qiang H, Wang W, Lin G (2019) Hopf bifurcation analysis of maglev vehicle–guideway interaction vibration system and stability control based on fuzzy adaptive theory. Comput Ind 108:197–209

    Google Scholar 

  6. Surya S, Ramyashree S, Nidhi R et al (2015) Development of a simple MAGLEV system for a low-speed wind tunnel. In: 2015 International conference on power and advanced control engineering (ICPACE). IEEE, pp 441–444

  7. Chuan M, Changsheng Z (2017) Unbalance compensation for active magnetic bearing rotor system using a variable step size real-time iterative seeking algorithm. IEEE Trans Ind Electron 65(5):4177–4186

    Google Scholar 

  8. Anuradha P, Rallapalli H, Narsimha G (2018) Energy efficient scheduling algorithm for the multicore heterogeneous embedded architectures. Des Autom Embed Syst 22(1–2):1–12

    Google Scholar 

  9. Kaleem Z, Yoon TM, Lee C (2015) Energy efficient outdoor light monitoring and control architecture using embedded system. IEEE Embed Syst Lett 8(1):18–21

    Google Scholar 

  10. de Souza RH, Savazzi S, Becker LB (2015) Network design and planning of wireless embedded systems for industrial automation. Des Autom Embed Syst 19(4):367–388

    Google Scholar 

  11. Greatwood C, Richards AG (2019) Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control. Auton Robots 43:1681–1693

    Google Scholar 

  12. Tutuncu K, Ozcan R (2019) Embedded fuzzy logic control system for refrigerated display cabinets. Arab J Sci Eng 44:9529–9543

    Google Scholar 

  13. Gupta C, Tewari VK, Kumar AA et al (2019) Automatic tractor slip-draft embedded control system. Comput Electron Agric 165:1–11

    Google Scholar 

  14. Hidalgo MC, Garcia C, Angélico BA et al (2019) Embedded sliding mode controller applied to control valves with high friction. J Control Autom Electr Syst 30(5):677–687

    Google Scholar 

  15. Sun Y, Qiang H, Xu J et al (2019) IoT-based online condition monitor and improved adaptive fuzzy control for a medium-low-speed maglev train system. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2019.2938145

    Article  Google Scholar 

  16. Mantere M, Uusitalo I, Sailio M et al (2012) Challenges of machine learning based monitoring for industrial control system networks. In: 26th International conference on advanced information networking and applications workshops. IEEE, pp 968–972

  17. Zhu X, Guan C, Wu J et al (2006) Expectation-maximization method for EEG-based continuous cursor control. EURASIP J Adv Signal Process 2007(1):1–10

    Google Scholar 

  18. Sze V, Chen YH, Yang TJ et al (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

    Google Scholar 

  19. Zeng Z, Pantic M, Roisman GI et al (2008) A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58

    Google Scholar 

  20. Yan C, Xie H, Yang D et al (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295

    Google Scholar 

  21. Ji S, Xu W, Yang M et al (2012) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Google Scholar 

  22. Umer S, Dhara BC, Chanda B (2019) Face recognition using fusion of feature learning techniques. Measurement 146:43–54

    Google Scholar 

  23. Ye L, Yao C, Tao L, Cai R, Gong X (2018) Convolutional neural network construction method for embedded FPGAs oriented edge computing. J Comput Res Dev 55(3):551–562

    Google Scholar 

  24. Wan L, Jinning D, Sihui C et al (2019) Design and simulation of Butterworth lowpass filter based on CFA. J Hubei Univ (Nat Sci) 41(3):313–317

    Google Scholar 

  25. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    MathSciNet  MATH  Google Scholar 

  26. Larochelle H, Bengio Y, Louradour J et al (2009) Exploring strategies for training deep neural networks. J Mach Learn Res 1(10):1–40

    MATH  Google Scholar 

  27. Aoyagi M (2010) Stochastic complexity and generalization error of a restricted Boltzmann machine in Bayesian estimation. J Mach Learn Res 11(1):1243–1272

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 51905380, by China Postdoctoral Science Foundation under Grant 2019M651582, by independent Research Project of State Key Laboratory (No. TPL1711) and Funded Project of National Key Research and Development Plan (2016YFB1200601).

Author information

Authors and Affiliations

  1. Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Ding-gang Gao & Shi-hui Luo

  2. Maglev Transportation Engineering R&D Center of Tongji University, Shanghai, 201804, China

    Ding-gang Gao, You-gang Sun & Guo-bin Lin

  3. CRRC Zhuzhou locomotive Co., Ltd., Zhuzhou, 412001, Hunan, China

    Lai-sheng Tong

Authors
  1. Ding-gang Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. You-gang Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shi-hui Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Guo-bin Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Lai-sheng Tong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to You-gang Sun.

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

Gao, Dg., Sun, Yg., Luo, Sh. et al. Deep learning controller design of embedded control system for maglev train via deep belief network algorithm. Des Autom Embed Syst 24, 161–181 (2020). https://doi.org/10.1007/s10617-020-09237-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10617-020-09237-3

Keywords