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

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

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

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
Volume8
Issue number4
Publication statusPublished - 2006 Dec 1

Fingerprint

Anti-lock braking systems
Neural Control
Fuzzy Control
Observer
Slip
Wheel
Wheels
Controller
Lyapunov Theory
Controllers
Output Feedback Control
Strictly positive
Closed loop systems
Closed-loop System
Feedback control
Weighting
Control Strategy
Tuning
Update
Vary

Keywords

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

ASJC Scopus subject areas

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

Cite this

Observer-based direct adaptive fuzzy-neural control for anti-lock braking systems. / Chen, Guan Ming; Wang, Wei-Yen; Lee, Tsu Tian; Tao, C. W.

In: International Journal of Fuzzy Systems, Vol. 8, No. 4, 01.12.2006, p. 208-218.

Research output: Contribution to journalArticle

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