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International Journal of Fuzzy Systems

, Volume 21, Issue 2, pp 676–684 | Cite as

Design of Optimal Disturbance Attenuation Controller for Networked TS Fuzzy Vehicle Active Suspension with Control Delay

  • Xiao-Fang Zhong
  • Shi-Yuan Han
  • Jin ZhouEmail author
  • Yue-Hui Chen
Article
  • 53 Downloads

Abstract

In this paper, a disturbance attenuation controller is presented for networked vehicle active suspension with measurement noise and random control delay. By defining the appearing probabilities of control delay as the membership function, the Takagi–Sugeno (TS) fuzzy model of networked vehicle active suspension is established with the consideration of the persistent irregular road disturbance, the random actuator delay, and the measurement noise. By designing a transformation vector, the disturbance attenuation control problem is reformulated as an equivalence two-point-boundary-value problem under the constrains of a delay-free system with respect to an equivalence performance index. After that, an optimal disturbance attenuation controller is proposed by solving a Riccati equation and a Stein equation, in which a Kalman filter is employed to estimate the road disturbance state with Gaussian white noise. Finally, by employing a simple vehicle active suspension, simulation results show that the designed controller can attenuate the vibration and compensate the control delay for the networked TS fuzzy vehicle active suspension, in which the values of the sprung mass acceleration, the suspension deflection and the tyre deflection can be reduced effectively.

Keywords

Vehicle active suspension Disturbance attenuation control TS fuzzy control Control delay Optimal control 

Notes

Acknowledgements

This work is supported by the Natural Science Foundation of Shandong Province (ZR2017MF044), the Shandong Province Key Research and Development Program (2018GGX101016, 2018GGX101048, 2017GGX10144), the Shandong Province Higher Educational Science and Technology Program (J17KA047, J16LN07, J16LB06, J15LN13), the Natural Science Foundation of China (61671220, 61702217).

References

  1. 1.
    Tseng, H.E., Hrovat, D.: State of the art survey: active and semi-active suspension control. Veh. Syst. Dyn. 53, 1034–1062 (2015)CrossRefGoogle Scholar
  2. 2.
    Sun, W., Pan, H., Zhang, Y., Gao, H.: Multi-objective control for uncertain nonlinear active suspension systems. Mechatronics 24, 318–327 (2014)CrossRefGoogle Scholar
  3. 3.
    Lian, R.-J.: Enhance adaptive self-organizing fuzzy sliding-mode controller for active suspension systems. IEEE Trans. Ind. Electron. 60, 958–968 (2013)CrossRefGoogle Scholar
  4. 4.
    Brezas, P., Smith, M.C.: Linear quadratic optimal and risk-sensitive control for vehicle active suspensions. IEEE Trans. Control Syst. Technol. 22, 543–566 (2014)CrossRefGoogle Scholar
  5. 5.
    Han, S.-Y., Zhang, C.-H., Tang, G.-Y.: Approximation optimal vibration for networked nonlinear vehicle active suspension with actuator time delay. Asian J. Control 19, 983–995 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Gohrle, C., Schindler, A., Sawodny, O.: Design and vehicle implementation of preview active suspension controller. IEEE Trans. Control Syst. Technol. 22, 1135–1142 (2014)CrossRefGoogle Scholar
  7. 7.
    Hu, Z., Deng, F.: Modeling and stabilization of networked control systems with bounded packet dropouts and occasionally missing control inputs subject to multiple sampling periods. J. Frankl. Inst. 354, 4675–4696 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Liang, X., Xu, J., Zhang, H.: Optimal control and stabilization for networked control systems with packet dropout and input delay. IEEE Trans. Circuits Syst. II Express Briefs 64, 1087–1091 (2017)CrossRefGoogle Scholar
  9. 9.
    Chen, J., Meng, S., Sun, J.: Stability analysis of networked control systems with aperiodic sampling and time-varying delay. IEEE Trans. Cybern. 46, 2312–2320 (2017)CrossRefGoogle Scholar
  10. 10.
    Wu, C., Liu, J., Jing, X., Li, H., Wu, L.: Adaptive fuzzy control for nonlinear networked control systems. IEEE Trans. Syst. Man Cybern. Syst. 47, 2420–2430 (2017)CrossRefGoogle Scholar
  11. 11.
    Li, H., Wu, C., Jing, X., Wu, L.: Fuzzy tracking control for nonlinear networked systems. IEEE Trans. Cybern. 46, 2020–2031 (2017)CrossRefGoogle Scholar
  12. 12.
    Ge, Y., Wang, J., Zhang, L., Wang, B., Li, C.: Robust fault tolerant control of distributed networked control systems with variable structure. J. Frankl. Inst. 353, 2553–2575 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Han, S.-Y., Chen, Y.-H., Tang, G.-Y.: Fault diagnosis and fault-tolerant tracking control for discrete-time systems with faults and delays in actuator and measurement. J. Frankl. Inst. 354, 4719–4738 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wen, S., Chen, Michael Z.Q., Zeng, Z., Yu, X., Huang, T.: Fuzzy control for uncertain vehicle active suspension systems via dynamic sliding-mode approach. IEEE Trans. Syst. Man Cybern. Syst. 47, 24–32 (2017)CrossRefGoogle Scholar
  15. 15.
    Moradi, M., Fekih, A.: Adaptive PID-sliding-mode fault-tolerant control approach for vehicle suspension systems subject to actuator faults. IEEE Trans. Veh. Technol. 63, 1041–1051 (2014)CrossRefGoogle Scholar
  16. 16.
    Ren, H., Chen, S., Zhao, Y., Liu, G., Yang, L.: State observer-based sliding mode control for semi-active hydro-pneumatic suspension. Veh. Syst. Dyn. 54, 194–216 (2016)CrossRefGoogle Scholar
  17. 17.
    Yue, W., Shi, Y., Peng, A., Li, S.: Study on ride comfort of a heavy vehicle based on active hydro-pneumatic suspension. J. Vib. Shock 35, 183–188 (2016)Google Scholar
  18. 18.
    Sun, W., Pan, H., Gao, H.: Filter-based adaptive vibration control for active vehicle suspensions with electrohydraulic actuators. IEEE Trans. Veh. Technol. 65, 4619–4626 (2016)CrossRefGoogle Scholar
  19. 19.
    Zhang, Y., Zhang, X., Zhan, M., Guo, K., Zhao, F., Liu, Z.: Study on a novel hydraulic pumping regenerative suspension for vehicles. J. Frankl. Inst. 352, 485–499 (2015)CrossRefzbMATHGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiao-Fang Zhong
    • 1
    • 2
  • Shi-Yuan Han
    • 1
    • 2
  • Jin Zhou
    • 2
    Email author
  • Yue-Hui Chen
    • 2
  1. 1.School of Data and Computer ScienceShandong Women’s UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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