Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle

Wei-Yen Wang, Ming Chang Chen, Shun Feng Su

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

This paper proposes a novel method for identification and robust adaptive control of an anti-lock braking system with an active suspension system by using the hierarchical Takagi-Sugeno (T-S) fuzzy-neural model. The goal of a conventional ABS control system is to rapidly eliminate tracking error between the actual slip ratio and a set reference value in order to bring the vehicle to a stop in the shortest time possible. However, braking time and stopping distance can be reduced even further if the same control system also simultaneously considers the state of the active suspension system. The structure learning capability of the proposed hierarchical T-S fuzzy-neural network is exploited to reduce computational time, and the number of fuzzy rules. Thus, this proposed controller is applied to achieve integrated control over the anti-lock braking system (ABS) with the active suspension system. Our simulation results, presented at the end of this paper, show that the proposed controller is extremely effective in integrated control over the ABS and the active suspension system.

Original languageEnglish
Pages (from-to)1698-1706
Number of pages9
JournalAutomatica
Volume48
Issue number8
DOIs
Publication statusPublished - 2012 Aug 1

Keywords

  • Active suspension system
  • And anti-lock braking
  • Hierarchical FNNs
  • Robust adaptive control
  • Takagi-Sugeno

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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