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Semi-Active Predictive Control of Isolated Bridge Based on Magnetorheological Elastomer Bearing

  • Rui Li (李锐)
  • Mengjiao Zhou (周梦娇)
  • Mengjuan Wu (吴孟娟)
  • Xiaoming Tang (唐晓铭)Email author
Article
  • 12 Downloads

Abstract

Time-delay of magnetorheological elastomer bearing (MRB) can bring structural response menace to bridges. This paper investigates a bridge pier-bearing semi-active-coupling control method based on model predictive control (MPC). The presented strategy takes the structure prediction model to predict the state responses of the controlled plant in a period of future time. Then, the control law can be determined by solving a finite horizon optimization problem. The peak shearing force of pier top, the displacement and the acceleration of beam are chosen as control goals, and the vibration isolation rate is applied to characterize the vibration isolation effect. It is noted that MPC method naturally takes the time-delay and uncertain interference into consideration, and significantly improves the control performance of the system. Finally, the numerical example is described and the seismic response of isolated bridge based on MRB is analyzed. The simulation results show that predictive control can be used to control the time-delay of bridge system in different degrees. The best control performance is at 0.4 s. Even if the time-delay reaches 2 s, it is still good. Therefore, the control method significantly reduces the adverse effects of time-delay on the system, and has a good vibration isolation performance.

Key words

bridge magnetorheological elastomer bearing (MRB) predictive control time-delay 

CLC number

TP 18 U 447 

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References

  1. [1]
    CHEN L K, JIANG L Z, YU Z W, et al. Seismic response characteristics of a high-speed railway simplysupported girder bridge [J]. Journal of Vibration and Shock, 2011, 30(12): 216–222 (in Chinese).Google Scholar
  2. [2]
    MA Y Q, QIU H X. Fuzzy neural network control to suppress seismic response of continuous girder railway bridge using new magneto rheological grease damper [J]. Journal of Vibration and Shock, 2015, 34(2): 66–73 (in Chinese).Google Scholar
  3. [3]
    CARDEN L P, BUCKLE I G, ITANI A M. Transverse displacement capacity and stiffness of steel plate girder bridge superstructures for seismic loads [J]. Journal of Constructional Steel Research, 2007, 63(11): 1546–1559.Google Scholar
  4. [4]
    LI R, ZHOU H L, CHEN S W, et al. Human-simulated adaptive control of the train braking response on the isolated bridge [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2016, 44(8): 29–35 (in Chinese).zbMATHGoogle Scholar
  5. [5]
    YANG M G, CAI C S. Longitudinal vibration control for a suspension bridge subjected to vehicle braking forces and earthquake excitations based on magnetorheological dampers [J]. Journal of Vibration and Control, 2015, 22(17): 1–20.Google Scholar
  6. [6]
    ZHUANG J S. Some problems about bridge seismic isolation bearings [J]. Prestress Technology, 2012(5): 30–40 (in Chinese).Google Scholar
  7. [7]
    CHEN S W, LI R, ZHANG Z, et al. Micromechanical analysis on tensile modulus of structured magnetorheological elastomer [J]. Smart Materials and Structures, 2016, 25(3): 035001.Google Scholar
  8. [8]
    LI R, DU P F, LI Y F, et al. Magneto-rheological vibration isolation model experimental study on discrete floating slab based on similarity theory [J]. Chinese Journal of Scientific Instrument, 2014, 35(1): 200–207 (in Chinese).Google Scholar
  9. [9]
    WANG L, HUANG Z, ZHOU D. Semi active control of structural vibration based on prediction algorithm [J]. Journal of Vibration and Shock, 2007, 26(10): 109–112 (in Chinese).Google Scholar
  10. [10]
    ZHANG C, ZHAO Y. Development and application of model based predictive control theory [J]. Electronic Technology and Software Engineering, 2014(4): 256–257 (in Chinese).Google Scholar
  11. [11]
    HUANG H. Robust stability of nonlinear model predictive control and online optimization algorithms [D]. Hangzhou, China: College of Information Engineering, Zhejiang University of Technology, 2013 (in Chinese).Google Scholar
  12. [12]
    XU J G, WANG Z M, HE Y A. Study of semi-active control of structure based on predictive control [J]. World Earthquake Engineering, 2003, 19(2): 126–131 (in Chinese).Google Scholar
  13. [13]
    BEHROOZ M, WANG X J, GORDANINEJAD F. Modeling of a new semi-active/passive magnetorheological elastomer isolator [J]. Smart Materials & Structures, 2014, 23(4): 045013.Google Scholar
  14. [14]
    SHEN J, TSAI M H, CHANG K C, et al. Performance of a seismically isolated bridge under near-fault earthquake ground motions [J]. Journal of Structural Engineering, 2004, 130(6): 861–868.Google Scholar
  15. [15]
    ZHAO L, ZHAO M X. Time delay and compensation for structural vibration active control system [J]. Industrial Construction, 2006, 36(sup): 1619–1625 (in Chinese).Google Scholar
  16. [16]
    XU L H. Semi-active predictive control of tall building [J]. Journal of Tianjin University, 2008, 41(4): 482–487.Google Scholar
  17. [17]
    XU L H, LI Z X, QIAN J R. Time delay and compensation for semi-active predictive control system [J]. Engineering Mechanics, 2011, 28(9): 79–83 (in Chinese).Google Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Rui Li (李锐)
    • 1
  • Mengjiao Zhou (周梦娇)
    • 1
  • Mengjuan Wu (吴孟娟)
    • 1
  • Xiaoming Tang (唐晓铭)
    • 1
    Email author
  1. 1.Department of AutomationChongqing University of Posts and TelecommunicationsChongqingChina

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