Fault estimation methods for semi-active suspension systems

Diana Hernandez-Alcantara, Ruben Morales-Menendez, Luis Amezquita-Brooks, Olivier Sename, Luc Dugard

Research output: Contribution to conferencePaper

6 Citations (Scopus)


Semi-Active suspension systems aim to improve the stability and comfort of vehicles. Although, they offer better performance than passive suspensions, the actuators such as magneto-rheological dampers are more susceptible to failure. Oil leakage is the most common fault, and its effect is a reduction of the damping force. The estimation of suspension faults can be used with a Fault Tolerant Control system to prevent handling and comfort deterioration. However, fault estimation schemes introduce additional challenges due to the damper non-linear dynamics and the strong influence of the disturbances (i.e the road profile). One of the first obstacles for appropriate damper fault detection is the modeling of the fault, which has been shown to be of multiplicative nature. However, many of the most widespread fault detection schemes consider additive faults due to mathematical convenience. Two model-based fault estimation schemes for semi-active dampers are proposed: an observer-based approach, which is intended to estimate additive faults; and a parameter identification approach, which is intended to estimate multiplicative faults. The performance of these schemes is validated and compared through simulations using a commercial vehicle model. Early results shows that a parameter identification approach is more accurate in fault estimation, whereas an observer-based approach is less sensible to parametric uncertainty.

Original languageEnglish
Publication statusPublished - 29 Jan 2016
Externally publishedYes
Event2015 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2015 -
Duration: 29 Jan 2016 → …


Conference2015 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2015
Period29/1/16 → …

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Copyright 2016 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Energy Engineering and Power Technology


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