Advertisement

Journal of Mechanical Science and Technology

, Volume 34, Issue 1, pp 43–54 | Cite as

Fuzzy adaptive control particle swarm optimization based on T-S fuzzy model of maglev vehicle suspension system

  • Chen Chen
  • Junqi XuEmail author
  • Guobin Lin
  • Yougang Sun
  • Dinggang Gao
Original Article

Abstract

At present, with the gradual promotion of Maglev vehicles, the stability of the suspension system has gradually become a hotspot. During the operation of Maglev vehicles, vibration may be caused by external disturbances such as track irregularity, non-directional wind load and load variation. When the vibration amplitude is within the controllable range of the current parameters, the restraint effect can be achieved and the stable convergence can be formed. However, when the vibration amplitude exceeds the current controllable range, the maglev vehicle may break the track or even lose stability. In order to solve the possible adverse effects of external disturbances on the stability of the system, a T-S fuzzy model considering both parameter uncertainties and external disturbances is constructed, and a relatively mature fuzzy adaptive control method is used for suspension control. However, considering the tracking performance of the system control parameters and the response speed of the parameter changes when the external disturbance changes, the particle swarm optimization (PSO) algorithm is used to optimize the system. The effectiveness of the optimized fuzzy adaptive control law in coordinating the closed-loop stability of the suspension system is proved in terms of response speed and convergence performance. Based on linear matrix inequality (LMI), the control response region satisfying the control performance after optimization is defined, and Lyapunov method is adopted to prove the stability of the optimized algorithm in controlling vehicle fluctuation operation. The simulation and experimental results show that the fuzzy adaptive control algorithm optimized by particle swarm optimization can further improve the speed of parameter optimization and the tracking performance of the system in the face of external disturbances and internal system parameter perturbations within a given range of control parameters. Compared with previous control strategies, the controller can greatly improve the response speed and the closed-loop information updating ability of the system in the face of disturbances, so that the system has stronger robustness and faster dynamic response.

Keywords

Maglev vehicle Levitation system T-S model Fuzzy adaptive control PSO Coupled vibration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research is supported by the National Key Technology R&D Program of the 13th Five-year Plan, Research on Key Technologies of Medium Speed Maglev Transportation System (2016YB1200601) and The Fundamental Research Funds for the Central Universities.

References

  1. [1]
    H. W. Lee, K. C. Kim and J. Lee, Review of Maglev train technologies, IEEE Transactions on Magnetics, 42 (7) (2006) 1917–1925.CrossRefGoogle Scholar
  2. [2]
    Y. G. Sun et al., An experimental study on the vibration of the low-speed Maglev train moving on the guideway with sag vertical curves, International Journal of Control and Automation, 9 (4) (2016) 279–288.CrossRefGoogle Scholar
  3. [3]
    R. D. Thornton, Efficient and affordable Maglev opportunities in the united states, Proceedings of The IEEE, 97 (11) (2009) 1901–1921.CrossRefGoogle Scholar
  4. [4]
    Z. Q. Long, A. M. Hao and W. S. Chang, Suspension controller design of Maglev train considering the rail track periodical irregularity, Journal of National University of Defense Technology, 25 (2) (2003) 84–89.Google Scholar
  5. [5]
    J. D. Lindlau and C R Knospe, Feedback linearization of an active magnetic bearing with voltage control, IEEE Transactions on Control Systems Technology, 10 (1) (2002) 21–31.CrossRefGoogle Scholar
  6. [6]
    W. R. Song, G. F. Yu and Y. F. Wang, PID control of micro feed mechanism based on magnetic levitation technology, Journal of Harbin Institute of Technology, 36 (1) (2004) 28–31.Google Scholar
  7. [7]
    L. M. Dai et al., PID control and experiment for magnetism levitation movement system, Modern Manufacturing Engineering, 6 (2008) 79–82.Google Scholar
  8. [8]
    H. Wang, X. B. Zhang and G. Su. A new Maglev line system design and control strategy, Journal of Tongji University (Natural Science), 41 (7) (2013) 1112–1118.Google Scholar
  9. [9]
    Y. G. Sun et al., Dynamic modeling and control of nonlinear electromagnetic suspension systems, Chemical Engineering Transactions, 46 (2015) 1039–1045.Google Scholar
  10. [10]
    G. He, J. Li and P. Cui, Nonlinear control scheme for the levitation module of Maglev train, Journal of Dynamic Systems Measurement and Control-Transactions of the ASME, 138 (7) (2016) 1–8.CrossRefGoogle Scholar
  11. [11]
    X. Su et al., Fuzzy control of nonlinear electromagnetic suspension systems, Mechatronics, 24 (4) (2014) 328–335.CrossRefGoogle Scholar
  12. [12]
    H. Wang, X. B. Zhong and G. Shen, Analysis and experimental study on the MAGLEV vehicle-guideway interaction based on the full-state feedback theory, Journal of Vibration and Control, 12 (2) (2015) 51–74.Google Scholar
  13. [13]
    H. Y. Qiang, W. L. Li, Y. G. Sun and X. Y. Liu, Levitation chassis dynamic analysis and robust position control for maglev vehicles under nonlinear periodic disturbance, Journal of Vibroengineering, 19 (2) (2017) 1273–1286.CrossRefGoogle Scholar
  14. [14]
    J. H. Li et al., The active control of Maglev stationary selfexcited vibration with a virtual energy harvester, IEEE Transactions on Industrial Electronics, 62 (5) (2015) 2942–2951.CrossRefGoogle Scholar
  15. [15]
    Y. G. Sun et al., Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems, Neural Computing and Applications (2017) Doi: 10.1007/s00521-017-2983-y.Google Scholar
  16. [16]
    C. Chen et al., Sliding mode robust adaptive control of Maglev vehicle’s nonlinear suspension system based on flexible track: Design and experiment, IEEE Access (2019) 1–1.Google Scholar
  17. [17]
    L. L. Yang and J. M. Li, Multi-input discrete T-S fuzzy bilinear model and fuzzy controller design, Fuzzy Systems and Mathematics, 25 (5) (2011) 96–100.MathSciNetzbMATHGoogle Scholar
  18. [18]
    X. Y. Liu, Robust Adaptive Fuzzy Control for a Class of Nonlinear Systems, Wuhan: Wuhan University of Science and Technology (2002).Google Scholar
  19. [19]
    J.-B. Han et al., Dynamic modeling and simulation of EMS Maglev vehicle to evaluate the levitation stability and operational safety over an elastic segmented switch track, Journal of Mechanical Science and Technology, 32 (7) (2018) 2987–2998.CrossRefGoogle Scholar
  20. [20]
    J. H. Jeong et al., Feedforward compensation of back electromotive force for suppressing rotational motion errors in a magnetically levitated system, Journal of Mechanical Science and Technology, 31 (10) (2017) 4619–4630.CrossRefGoogle Scholar
  21. [21]
    G. C. Lee et al., Accelerated lifetime test using a moment bar for a base frame of an aerial-work-platform vehicle, Journal of Mechanical Science & Technology, 31 (8) (2017) 3853–3860.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Chen Chen
    • 1
    • 2
    • 3
  • Junqi Xu
    • 2
    Email author
  • Guobin Lin
    • 2
  • Yougang Sun
    • 1
    • 2
    • 3
  • Dinggang Gao
    • 2
    • 4
  1. 1.The Key Laboratory of Road and Traffic Engineering, Ministry of EducationTongji UniversityShanghaiChina
  2. 2.Maglev Transportation Engineering R&D CenterTongji UniversityShanghaiChina
  3. 3.College of Transportation EngineeringTongji UniversityShanghaiChina
  4. 4.Traction Power State Key LaboratorySouthwest Jiaotong UniversityChengduChina

Personalised recommendations