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Adaptive Fuzzy Neural Network Control for Transient Dynamics of Magneto-rheological Suspension with Time-Delay

  • Xiaomin Dong
  • Miao Yu
  • Changrong Liao
  • Weimin Chen
  • Honghui Zhang
  • Shanglian Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Since Magneto-rheological (MR) suspension has nonlinearity and time-delay, the application of linear feedback strategy has been limited. This paper addresses the problem of control of MR suspension with time-delay when transient dynamics are presented. An adaptive Fuzzy-Neural Network Control (FNNC) scheme for the transient course is proposed using fuzzy logic control and artificial neural network methodologies. To attenuate the adverse effects of time-delay on control performance, a Time Delay Compensator (TDC) is established. Then, through a numerical example of a quarter car model and a real road test with a bump input, the comparison is made between passive suspension and semi-active suspension. The results show that the MR vehicle with FNNC strategy can depress the peak acceleration and shorten the setting time, and the effect of time-delay can be attenuated. The results of road test with the similarity of numerical study verify the feasibility of the control strategy.

Keywords

Ride Comfort Road Test Passive Suspension Time Delay Compensator Recurrent Fuzzy Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaomin Dong
    • 1
  • Miao Yu
    • 1
  • Changrong Liao
    • 1
  • Weimin Chen
    • 1
  • Honghui Zhang
    • 1
  • Shanglian Huang
    • 1
  1. 1.Center for Intelligent Structures, Dept. of Optoelectronic EngineeringChongqing UniversityChongqingChina

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