Observer-based direct adaptive fuzzy-neural control for anti-lock braking systems

Guan Ming Chen, Wei Yen Wang*, Tsu Tian Lee, C. W. Tao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


In this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the constraint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output feedback control law and update law for on-line tuning of the weighting factors of the direct adaptive fuzzy-neural controller are derived. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed control scheme for ABS control.

Original languageEnglish
Pages (from-to)208-218
Number of pages11
JournalInternational Journal of Fuzzy Systems
Issue number4
Publication statusPublished - 2006 Dec
Externally publishedYes


  • Adaptive control
  • Anti-lock braking system
  • Fuzzy control
  • Neural networks
  • Nonlinear systems
  • Observer
  • Slip ratio

ASJC Scopus subject areas

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


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