Skip to main navigation Skip to search Skip to main content

Fuzzy skyhook surface control using micro-genetic algorithm for vehicle suspension ride comfort

  • Yi Chen*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

Abstract

A polynomial function supervised fuzzy sliding mode control (PSFαSMC), collaborated with a skyhook surface method, is presented for the ride comfort of a vehicle semi-active suspension. The multi-objectivemicro- genetic algorithm (MOμGA) has been utilised to the PSFαSMC controller's parameter alignment in a training process with three ride comfort objectives for the vehicle semi-active suspension, which is called the 'offline' step. Then, the optimised parameters are applied to the real-time control process by the polynomial function supervised controller, which is named 'online' step. A two degree of freedom dynamic model of a vehicle semi-active suspension system is given for passenger's ride comfort enhancement studies and a simulation with the given initial conditions has been devised in MATLAB/SIMULINK. The numerical results have shown that this hybrid control method is able to provide a real-time enhanced level of ride comfort performance for the semi-active suspension system.

Original languageEnglish
Title of host publicationIntelligent Computational Optimization in Engineering
Subtitle of host publicationTechniques and Applications
EditorsMario Koppen, Gerald Schaefer, Ajith Abraham
Pages357-394
Number of pages38
DOIs
StatePublished - 2011
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume366
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Fuzzy skyhook surface control using micro-genetic algorithm for vehicle suspension ride comfort'. Together they form a unique fingerprint.

Cite this