Advertisement

Nonlinear Dynamics

, Volume 59, Issue 3, pp 433–453 | Cite as

Comparative research on semi-active control strategies for magneto-rheological suspension

  • Xiao-min Dong
  • Miao Yu
  • Chang-rong Liao
  • Wei-min Chen
Original Paper

Abstract

This paper presents the comparison results of a study to identify an appropriate semi-active control algorithm for a MR suspension system from a variety of semi-active control algorithms for use with MR dampers. Five representative control algorithms are considered including the skyhook controller, the hybrid controller, the LQG controller, the sliding mode controller and the fuzzy logic controller. To compare the control performances of the five control algorithms, a quarter car model with a MR damper is adopted as the baseline model for our analysis. After deriving the governing motion equations of the proposed dynamic model, five controllers are developed. Then each control policy is applied to the baseline model equipped with a MR damper. The performances of each control algorithm under various road conditions are compared along with the equivalent passive model in both time and frequency domains through the numerical simulation. Subsequently, a road test is performed to validate the actual control performance. The results show that the performance of a MR suspension system is highly dependent on the choice of algorithm employed, and the sliding mode control strategy exhibits an excellent integrated performance.

Keywords

Skyhook Hybrid LQG Sliding mode control Fuzzy logic control Magneto-rheological suspension 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hassan, S.A., Sharp, R.S.: The relative performance capability of passive, active and semi-active car suspension systems. SAE technical paper series 864901 (1986) Google Scholar
  2. 2.
    Lou, Z., Ervin, R.D., Filisko, F.E.: A preliminary parametric study of electrorheological dampers. Trans. ASME J. Fluids Eng. 116(3), 570–576 (1994) CrossRefGoogle Scholar
  3. 3.
    Sassi, S., Cherif, K., Mezghani, L., Thomas, M., Kotrane, A.: An innovative magnetorheological damper for automotive suspension: from design to experimental characterization. Smart Mater. Struct. 14, 811–822 (2005) CrossRefGoogle Scholar
  4. 4.
    Lam, A.H.-F., Liao, W.-H.: Semi-active control of automotive suspension systems with magneto-rheological dampers. Int. J. Veh. Des. 33(1/2/3), 50–75 (2003) CrossRefGoogle Scholar
  5. 5.
    Nguyen, Q.H., Choi, S.B.: Optimal design of MR shock absorber and application to vehicle suspension. Smart Mater. Struct. 18(3), 035012 (2009) CrossRefGoogle Scholar
  6. 6.
    Lee, H.S., Choi, S.B.: Control and response characteristics of a magneto-rheological fluid damper for passenger vehicles. J. Intell. Mater. Syst. Struct. 11(1), 80–87 (2000) Google Scholar
  7. 7.
    Choi, S.B., Lee, H.S., Park, Y.P.: H-infinity control performance of a full-vehicle suspension featuring magnetorheological dampers. Veh. Syst. Dyn. 38(5), 341–360 (2002) CrossRefGoogle Scholar
  8. 8.
    Batterbee, D.C., Sims, N.D.: Hardware-in-the-loop simulation of magnetorheological dampers for vehicle suspension systems. Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 221(2), 265–278 (2007) CrossRefGoogle Scholar
  9. 9.
    Crosby, M.J., Harwood, R.A., Karnopp, D.: Vibration control using semi-active force generators. Lord Library of Technical Articles LL-7004 (1973) Google Scholar
  10. 10.
    Ahmadian, M., Simon, D.E.: An analytical and experimental evaluation of magneto rheological suspensions for heavy trucks. Veh. Syst. Dyn. 37, 38–49 (2002) CrossRefGoogle Scholar
  11. 11.
    Ahmadian, M., Vahdati, N.: Transient dynamics of semiactive suspensions with hybrid control. J. Intell. Mater. Syst. Struct. 17(2), 145–153 (2006) CrossRefGoogle Scholar
  12. 12.
    Wang, E.R., Ma, X.Q., Rakheja, S., Su, C.Y.: Semi-active control of vehicle vibration with MR-dampers. In: Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, HI, December (2003) Google Scholar
  13. 13.
    Guo, D.L., Hu, H.Y., Yi, J.Q.: Neural network control for a semi-active vehicle suspension with a magnetorheological damper. J. Vib. Control 10(3), 461–471 (2004) zbMATHCrossRefGoogle Scholar
  14. 14.
    Yagiz, N., Sakman, L.E.: Robust sliding mode control of a full vehicle without suspension gap loss. J. Vib. Control 11(11), 1357–1374 (2005) CrossRefGoogle Scholar
  15. 15.
    Yu, M., Liao, C.R., Chen, W.M., Huang, S.L.: Study on MR semi-active suspension system and its road testing. J. Intell. Mater. Syst. Struct. 17(8–9), 801–806 (2006) CrossRefGoogle Scholar
  16. 16.
    Eslaminasab, N., Biglarbegian, M., Melek, W.W., Golnaraghi, M.F.: A neural network based fuzzy control approach to improve ride comfort and road handling of heavy vehicles using semi-active dampers. Int. J. Heavy Veh. Syst. 14(2), 135–157 (2007) CrossRefGoogle Scholar
  17. 17.
    Choi, S.B., Lee, S.-K., Park, Y.P.: A hysteresis model for the field-dependent damping force of a magnetorheological damper. J. Sound Vib. 245(2), 375–383 (2001) CrossRefGoogle Scholar
  18. 18.
    Sung, K.G., Han, Y.M., Lim, K.H., Choi, S.B.: Discrete-time fuzzy sliding mode control for a vehicle suspension system featuring an electrorheological fluid damper. Smart Mater. Struct. 16(3), 798–808 (2007) CrossRefGoogle Scholar
  19. 19.
    Fang, X., Chen, W., Wu, L., Wang, Q., Fan, D., Li, Z.: Fuzzy control technology and the application to vehicle semi-active suspension. Chinese J. Mech. Eng. 35(3), 98–100 (1999) Google Scholar
  20. 20.
    Dong, X.M., Liao, C.R., Chen, W.M., Zhang, H.H., Huang, S.L.: Adaptive fuzzy neural network control for transient dynamics of magneto-rheological suspension with time-delay. In: Adv. Neural Netw. ISNN 2006, Pt. 2, Proc. Lecture Notes in Computer Science, vol. 3972, pp. 1046–1051. Springer, Berlin (2006) CrossRefGoogle Scholar
  21. 21.
    Yang, J.W., Li, J., Du, Y.P.: Adaptive fuzzy control of lateral semi-active suspension for high-speed railway vehicle. In: Comput. Intell., Pt. 2, Proc. Lecture Notes in Computer Science, vol. 4114, pp. 1104–1115. Springer, Berlin (2006) Google Scholar
  22. 22.
    Ying, Z.G., Zhu, W.Q., Soong, T.T.: A stochastic optimal semi-active control strategy for ER/MR damper. J. Sound Vib. 259(1), 45–62 (2003) CrossRefMathSciNetGoogle Scholar
  23. 23.
    Sims, N.D., Peel, D.J., Stanway, R., Johnson, A.R., Bullough, W.A.: The electrorheological long-stroke damper: a new modeling technique with experimental validation. J. Sound Vib. 229(2), 207–227 (2000) CrossRefGoogle Scholar
  24. 24.
    Spencer, B.F., Dyke, S.J., Sain, M.K., Carlson, J.D.: Phenomenological model for magnetorheological dampers. J. Eng. Mech. 123(3), 230–238 (1997) CrossRefGoogle Scholar
  25. 25.
    Chang, C.C., Roschke, P.: Neural network modeling of a magnetorheological damper. J. Intell. Mater. Syst. Struct. 9(9), 755–764 (1998) CrossRefGoogle Scholar
  26. 26.
    Ahmadian, M.: A hybrid semi-active control for secondary suspension applications. In: ASME International Congress and Exposition, November 16–21, Dallas, TX (1997) Google Scholar
  27. 27.
    Yokoyama, M., Hendrick, J.K., Toyama, S.: A model following sliding mode controller for semi-active suspension systems with MR dampers. In: Proceedings of the American Control Conference Arlington, VA, June 25–27, pp. 2652–2657 (2001) Google Scholar
  28. 28.
    Zhao, H., Lu, S.: A vehicle’s time domain model with road input on four wheels. Automot. Eng. 21(2), 112–117 (1999) Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Xiao-min Dong
    • 1
  • Miao Yu
    • 2
  • Chang-rong Liao
    • 2
  • Wei-min Chen
    • 2
  1. 1.State Key Laboratory of Mechanic TransmissionChongqing UniversityChongqingChina
  2. 2.College of Opto-Electronic Engineering, Key Lab of Optoelectronic Technology and System of Education MinistryChongqing UniversityChongqingChina

Personalised recommendations