Detection of critical situations and robust automotive fault tolerant control
type: Dataset
description: Modern vehicles are increasingly equipped with new mechanisms to improve occupant safety. These new systems are often active parts using data from sensors on the vehicle. However, in case of malfunction of a sensor, the consequences for the vehicle can be dramatic. To ensure safety in the vehicle, new methodologies for detection of faults suitable for vehicles are proposed. The developed methodologies are extended from the method of parity space for linear parameter varying systems (LPV). In addition, the transformation of fault detection problem for the detection of critical situations is also available. Application of results achieved on a real vehicle within the INOVE project illustrate the performance of fault detection and detection of loss of stability of the vehicle.
identifier: NNT: 2013GRENT029
issued: 2026-05-07T07:08:38.581821
language: fr
modified: 2026-05-07T07:08:38.581827
contact point: Organization
  fn: CCSD
keyword: detection-de-defauts
keyword: detection-de-situations-critiques
keyword: detection-of-critical-situation
keyword: dynamique-du-vehicule
keyword: espace-de-parite
keyword: fault-detection
keyword: infoeu-reposemanticsdoctoralthesis
keyword: lpv
keyword: parity-space
keyword: spiotherengineering-sciences-physicsother
keyword: stabilite-du-vehicule
keyword: theses
keyword: vehicle-dynamics
keyword: vehicle-stability
landing page: https://theses.hal.science/tel-00935257

Detection of critical situations and robust automotive fault tolerant control
type: Distribution
format: HTML
issued: 2026-05-07T07:08:38.585802
modified: 2026-05-07T07:08:38.566077
access URL: https://theses.hal.science/tel-00935257

test_moissonnage_selune
type: Agent