Robust T-S fuzzy-neural control of uncertain active suspension systems

Ming Chang Chen, Wei Yen Wang*, Shun Feng Su, Yi Hsing Chien

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


This paper proposes a novel method for identification of a class of the uncertain active suspension systems by using on-line adaptive T-S fuzzy-neural controller. In reality, vehicles may encounter unpredictable road conditions, e.g., rocks and potholes influencing the dynamic behavior of active suspension systems. These road conditions are not only cause parts of the active suspension system to fail, but also turn them into uncertain systems. To solve this problem, this paper uses the mean value theorem to transform the active suspension system, which is nonlinear, into a virtual linear system. Furthermore, the proposed robust controller design is used to compensator the modeling errors and the external disturbances. Then the T-S fuzzy-neural network can identify the dynamic model of the uncertain active suspension systems. Finally, the results of simulation are illustrated that the proposed controller design presents good performances and effectiveness.

Original languageEnglish
Pages (from-to)321-329
Number of pages9
JournalInternational Journal of Fuzzy Systems
Issue number4
Publication statusPublished - 2010 Dec


  • And robust control
  • T-S fuzzy-neural model
  • Uncertain active suspension systems

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence


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