Skip to main content
SpringerLink
Log in

Neural adaptive coordination control of multiple trains under bidirectional communication topology

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

Abstract

This paper investigates the problem of coordination control for a group of trains by neural adaptive approach. The communication structure among trains is a bidirectional one, i.e., necessary information of neighboring trains is used in the control design for a train. Two control schemes are developed, with the first one requiring the information of position, speed, and acceleration of neighboring trains, while the second requiring the information of position of neighboring trains only by virtue of high-order sliding mode observer technique. Based on the universal approximation capacity of radial basis function neural networks, there are no requirements of the precise parameters describing operational resistance and other kinds of extra resistances in the controller design, which are reconstructed by radial basis function neural networks online. The stability of single train and multiple trains are guaranteed by Lyapunov stability theorem. Numerical simulations are presented to demonstrate the effectiveness and performance of the proposed controllers.

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

Similar content being viewed by others

References

  1. Chong KT, Roh DH et al (2001) A Lyapunov function approach to longitudinal control of vehicles in a platoon. IEEE Trans Veh Technol 50(1):116–124

    Article  Google Scholar 

  2. Gao S, Dong H, Chen Y, Ning B, Chen G, Yang X (2013) Approximation-based robust adaptive automatic train control: an approach for actuator saturation. IEEE Trans Intell Transp Syst 14(4):1733–1742

    Article  Google Scholar 

  3. Gao S, Dong H, Ning B (2014) Characteristic model-based all-coefficient adaptive control for automatic train control systems. Sci China Inf Sci 57(9):1–12

    Article  Google Scholar 

  4. Gao S, Dong H, Ning B, Chen L (2015) Neural adaptive control for uncertain nonlinear system with input saturation: state transformation based output feedback. Neurocomputing 159:117–125

    Article  Google Scholar 

  5. Haykin S (2010) Neural networks: a comprehensive foundation, 1994. Mc Millan, New Jersey

    MATH  Google Scholar 

  6. Ioannou PA, Sun J (2012) Robust adaptive control. Courier Dover Publications, New York

    MATH  Google Scholar 

  7. Jain LC, Seera M, Lim CP, Balasubramaniam P (2014) A review of online learning in supervised neural networks. Neural Comput Appl 25(3–4):491–509

    Google Scholar 

  8. Khalil HK (2002) Nonlinear systems, 3rd edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  9. Khalil HK, Grizzle J (2002) Nonlinear systems, vol 3. Prentice hall, Upper Saddle River

    Google Scholar 

  10. Levant A (1998) Robust exact differentiation via sliding mode technique. Automatica 34(3):379–384

    Article  MathSciNet  MATH  Google Scholar 

  11. Levant A (2003) Higher-order sliding modes, differentiation and output-feedback control. Int J Control 76(9–10):924–941

    Article  MathSciNet  MATH  Google Scholar 

  12. Levant A, Livne M (2015) Uncertain disturbances attenuation by homogeneous multi-input multi-output sliding mode control and its discretisation. IET Control Theory Appl 9(4):515–525

    Article  MathSciNet  Google Scholar 

  13. Li S, Sun H, Yang J, Yu X (2015) Continuous finite-time output regulation for disturbed systems under mismatching condition. IEEE Trans Autom Control 60(1):277–282

    Article  MathSciNet  Google Scholar 

  14. Peters AA, Middleton RH, Mason O (2014) Leader tracking in homogeneous vehicle platoons with broadcast delays. Automatica 50(1):64–74

    Article  MathSciNet  MATH  Google Scholar 

  15. Seiler P, Pant A, Hedrick K (2004) Disturbance propagation in vehicle strings. IEEE Trans Autom Control 49(10):1835–1842

    Article  MathSciNet  Google Scholar 

  16. Sheikholeslam S, Desoer CA (1993) Longitudinal control of a platoon of vehicles with no communication of lead vehicle information: a system level study. IEEE Trans Veh Technol 42(4):546–554

    Article  Google Scholar 

  17. Su S, Tang T, Chen L, Liu B (2014) Energy-efficient train control in urban rail transit systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, p 0954409713515648

  18. Su S, Tang T, Li X, Gao Z (2014) Optimization of multitrain operations in a subway system. IEEE Trans Intell Transp Syst 15(2):673–684

    Article  Google Scholar 

  19. Swaroop D, Hedrick J (1996) String stability of interconnected systems. IEEE Trans Autom Control 41(3):349–357

    Article  MathSciNet  MATH  Google Scholar 

  20. Swaroop D, Hedrick J, Chien C, Ioannou P (1994) A comparison of spacing and headway control laws for automatically controlled vehicles1. Veh Syst Dyn 23(1):597–625

    Article  Google Scholar 

  21. Wang H, Yu F, Zhu L, Tang T, Ning B (2014) Finite-state markov modeling for wireless channels in tunnel communication-based train control systems. IEEE Trans Intell Transp Syst 15(3):1083–1090

    Article  Google Scholar 

  22. Wang LY, Syed A, Yin GG, Pandya A, Zhang H (2014) Control of vehicle platoons for highway safety and efficient utility: consensus with communications and vehicle dynamics. J Syst Sci Complex 27(4):605–631

    Article  MathSciNet  MATH  Google Scholar 

  23. Xu L, Wang LY, Yin G, Zhang H (2014) Communication information structures and contents for enhanced safety of highway vehicle platoons. IEEE Trans Veh Technol 63(9):4206–4220

    Article  Google Scholar 

  24. Xun J, Ning B, Li Kp, Zhang Wb (2013) The impact of end-to-end communication delay on railway traffic flow using cellular automata model. Transp Res C Emerg Technol 35:127–140

    Article  Google Scholar 

  25. Yin J, Chen D, Li L (2014) Intelligent train operation algorithms for subway by expert system and reinforcement learning. IEEE Trans Intell Transp Syst 15(6):2561–2571

    Article  Google Scholar 

  26. Zhang L, Zhuan X (2014) Optimal operation of heavy-haul trains equipped with electronically controlled pneumatic brake systems using model predictive control methodology. IEEE Trans Control Syst Technol 22(1):13–22

    Article  Google Scholar 

  27. Zhao N, Roberts C, Hillmansen S (2014) The application of an enhanced brute force algorithm to minimise energy costs and train delays for differing railway train control systems. Proc Inst Mech Eng F J Rail Rapid Transit 228(2):158–168

    Article  Google Scholar 

  28. Zhu L, Yu F, Ning B, Tang T (2014) Design and performance enhancements in communication-based train control systems with coordinated multipoint transmission and reception. IEEE Trans Intell Transp Syst 15(3):1258–1272

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported jointly by the Fundamental Research Funds for Central Universities (No. 2015JBZ007), National Natural Science Foundation of China (No. 61233001, No. 61322307), the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University Research Program (No. RCS2014ZT18).

Author information

Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Shigen Gao, Hairong Dong & Bin Ning

  2. Birmingham Centre for Railway Research and Education, University of Birmingham, Edgbaston, B15 2TT, UK

    Clive Roberts & Lei Chen

Authors
  1. Shigen Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hairong Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Clive Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hairong Dong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, S., Dong, H., Ning, B. et al. Neural adaptive coordination control of multiple trains under bidirectional communication topology. Neural Comput & Applic 27, 2497–2507 (2016). https://doi.org/10.1007/s00521-015-2020-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-2020-y

Keywords

Search

Navigation