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Modeling and control of a nonlinear half-vehicle suspension system: a hybrid fuzzy logic approach

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Abstract

Modeling and control of vehicle suspension system are high noteworthy from safety to comfort. In this paper, an analytical nonlinear half-vehicle model which is included quadratic tire stiffness, cubic suspension stiffness, and coulomb friction is derived based on fundamental physics. A hybrid fuzzy logic approach which combines fuzzy logic and PID controllers is designed for reducing the vibration levels of passenger seat and vehicle body. Performances of designed controllers have been evaluated by numerical simulations. Comparisons with classical PID control, Fuzzy Logic Control (FLC) and Hybrid Fuzzy-PID control (HFPID) have also been provided. Results of numerical simulations are evaluated in terms of time histories of displacement and acceleration responses and ride index comparison. A good performance for the Hybrid Fuzzy-PID controller with coupled rules (HFPIDCR) is achieved in simulation studies despite the nonlinearities.

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References

  1. Sie, W.-T., Lian, R.-J., Lin, B.-F.: Enhancing grey prediction fuzzy controller for active suspension systems. Veh. Syst. Dyn. 44(5), 407–430 (2006)

    Article  Google Scholar 

  2. Pfeiffer, F.: Deregularization of a smooth system-example hydraulics. Nonlinear Dyn. 47, 219–233 (2007)

    Article  MATH  Google Scholar 

  3. Yung, V.Y.B., Cole, D.J.: Modelling high frequency force behaviour of hydraulic automotive dampers. Veh. Syst. Dyn. 44(1), 1–31 (2006)

    Article  Google Scholar 

  4. Duym, S.W.R.: Simulation tools, modeling and identification, for an automotive shock absorber in the context of vehicle dynamics. Veh. Syst. Dyn. 33, 261–285 (2000)

    Article  Google Scholar 

  5. Duym, S., Stiens, R., Reybrouck, K.: Evaluation of shock absorber models. Veh. Syst. Dyn. 27(2), 109–127 (1997)

    Article  Google Scholar 

  6. Nohtomi, S., Okada, K., Urabe, H., Horiuchi, S.: Simultaneous robust optimization of suspension and active control system of road vehicles for handling improvement. Veh. Syst. Dyn. 44, 904–912 (2006)

    Article  Google Scholar 

  7. Stein, G.J., Zahoransky, R., Mucka, P.: On dry friction modeling and simulation in kinematically excited oscillatory systems. J. Sound Vib. 311(1), 74–96 (2008)

    Article  Google Scholar 

  8. Guclu, R.: Fuzzy logic control of seat vibrations of a non-linear full vehicle model. Nonlinear Dyn. 40, 21–34 (2005)

    Article  MATH  Google Scholar 

  9. Sakman, L.E., Guclu, R., Yagiz, N.: Fuzzy logic control of vehicle suspensions with dry friction nonlinearity. Sadhana 30(5), 649–659 (2005)

    Article  Google Scholar 

  10. Yagiz, N., Hacioglu, Y., Taskin, Y.: Fuzzy sliding-mode control of active suspensions. IEEE Trans. Ind. Electron. 55(11), 3883–3890 (2008)

    Article  Google Scholar 

  11. Cao, J., Li, P., Liu, H.: An interval fuzzy controller for vehicle active suspension systems. IEEE Trans. Intell. Transp. Syst. 11(4), 885–895 (2010)

    Article  MathSciNet  Google Scholar 

  12. McGee, C.G., Haroon, M., Adams, D.E., Luk, Y.W.: A frequency domain technique for characterizing nonlinearities in a tire-vehicle suspension system. J. Vib. Acoust. 127, 61–76 (2005)

    Article  Google Scholar 

  13. Yagiz, N., Yuksek, I., Sivrioglu, S.: Robust control of active suspensions for a full vehicle model using sliding mode control. JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf. 43(2), 253–258 (2000)

    Google Scholar 

  14. Yagiz, N., Yuksek, I.: Sliding mode control of active suspensions for a full vehicle model. Int. J. Veh. Des. 26(2), 264–276 (2001)

    Google Scholar 

  15. Marzbanrad, J., Ahmadi, G., Zohoor, H., Hojjat, Y.: Stochastic optimal preview control of a vehicle suspension. J. Sound Vib. 275, 973–990 (2004)

    Article  MathSciNet  Google Scholar 

  16. Akçay, H., Türkay, S.: Influence of tire damping on mixed H2/H synthesis of half-car active suspensions. J. Sound Vib. 322, 15–28 (2009)

    Article  Google Scholar 

  17. Onat, C., Kucukdemiral, I.B., Sivrioglu, S., Yuksek, I.: LPV model based gain-scheduling controller for a full vehicle active suspension system. J. Vib. Control 13(11), 1629–1666 (2007)

    Article  MATH  Google Scholar 

  18. Onat, C., Kucukdemiral, I.B., Sivrioglu, S., Yuksek, I., Cansever, G.: LPV gain-scheduling controller design for a non-linear quarter-vehicle active suspension system. Trans. Inst. Meas. Control 31(1), 71–95 (2009)

    Article  Google Scholar 

  19. Tusset, A.M., Rafikov, M., Balthazar, J.M.: An intelligent controller design for magnetorheological damper based on a quarter-car model. J. Vib. Control 15(12), 1907–1920 (2009)

    Article  MathSciNet  Google Scholar 

  20. Dong, X.-M., Yu, M., Liao, C.-R., Chen, W.-M.: Comparative research on semi-active control strategies for magneto-rheological suspension. Nonlinear Dyn. 59, 433–453 (2010)

    Article  MATH  Google Scholar 

  21. Guclu, R., Gulez, K.: Neural network control of seat vibrations of a non-linear full vehicle model using PMSM. Math. Comput. Model. 47, 1356–1371 (2008)

    MATH  Google Scholar 

  22. Yoshimura, T., Nakaminami, K., Kurimoto, M., Hino, J.: Control Eng. Pract. 7, 41–47 (1999)

    Article  Google Scholar 

  23. Rao, M.V.C., Prahlad, V.: A tunable fuzzy logic controller for vehicle-active suspension systems. Fuzzy Sets Syst. 85, 11–21 (1997)

    Article  Google Scholar 

  24. Huang, S.-J., Chen, H.-Y.: Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control. Mechatronics 16, 607–622 (2006)

    Article  Google Scholar 

  25. Montezari-Gh, M., Soleymani, M.: Genetic optimization of a fuzzy active suspension system based on human sensitivity to the transmitted vibrations. Proc. Inst. Mech. Eng., Part D, J. Automob. Eng. 222, 1769–1780 (2008)

    Article  Google Scholar 

  26. Sharkawy, A.B.: Fuzzy and adaptive fuzzy control for the automobiles’ active suspension system. Veh. Syst. Dyn. 43(11), 795–806 (2005)

    Article  Google Scholar 

  27. Peng, J., Wang, J., Wang, Y.: Neural network based robust hybrid control for robotic system: an H approach. Nonlinear Dyn. (2011). doi:10.1007/ss11071-010-9902-4

    Google Scholar 

  28. Çetin, Ş., Akkaya, A.V.: Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system. Nonlinear Dyn. 61, 465–476 (2010)

    Article  MATH  Google Scholar 

  29. Parnichkun, M., Ngaecharoenkul, C.: Kinematics control of a pneumatic system by hybrid fuzzy PID. Mechatronics 11, 1001–1023 (2001)

    Article  Google Scholar 

  30. Cao, D., Song, X., Ahmadian, M.: Editors perspectives: road vehicle suspension design, dynamics, and control. Veh. Syst. Dyn. 49(1), 3–28 (2011)

    Article  Google Scholar 

  31. Gonçalves, J.P.C., Ambrosio, J.A.C.: Optimization of vehicle suspension systems for improved comfort of road vehicles using flexible multibody dynamics. Nonlinear Dyn. 34, 113–131 (2003)

    Article  MATH  Google Scholar 

  32. Mann, G.K.I., Hu, B.-G., Gosine, R.G.: Analysis of direct action fuzzy PID controller structures. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 29(3), 829–845 (1999)

    Article  Google Scholar 

  33. Yongquan, Y., Ying, H., Minghui, W., Bi, Z., Guokun, Z.: Fuzzy neural PID controller and tuning its weight factors using genetic algorithm based on different location crossover. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3709–3713 (2004)

    Google Scholar 

  34. Zuo, L., Nayfeh, S.A.: Low order continuous-time filters for approximation of the ISO 2631-1 human vibration sensitivity weightings. J. Sound Vib. 265, 459–465 (2003)

    Article  Google Scholar 

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Correspondence to Saban Cetin.

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Demir, O., Keskin, I. & Cetin, S. Modeling and control of a nonlinear half-vehicle suspension system: a hybrid fuzzy logic approach. Nonlinear Dyn 67, 2139–2151 (2012). https://doi.org/10.1007/s11071-011-0135-y

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  • DOI: https://doi.org/10.1007/s11071-011-0135-y

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