Fault diagnosis for an automotive suspension using particle filters

D.H. Alcantara, R. Morales-Menendez, L. Amezquita-Brooks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

To diagnose faults in automotive suspension systems a particle filters based approach was evaluated. The look-ahead Rao-Blackwell Particle Filter was tested to online monitor different oil leaks in a magneto-rheological shock absorber. The non-linear semi-active suspension was modelled through the Jump Markov Linear Gaussian framework. The feasibility of this approach has been analyzed in a simulation environment using different road profiles. Early results with high precision and low variance are promised; however, the computing time is a hard constraint for an online application.
Original languageEnglish
Title of host publication2016 European Control Conference, ECC 2016
Pages1898-1903
Number of pages6
ISBN (Electronic)9781509025916
DOIs
Publication statusPublished - 6 Jan 2017
Externally publishedYes

Publication series

Name2016 European Control Conference, ECC 2016

Bibliographical note

Publisher Copyright:
© 2016 EUCA.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Fault diagnosis for an automotive suspension using particle filters'. Together they form a unique fingerprint.

Cite this