Detection of critical situations and robust automotive fault tolerant control

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.

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Source https://theses.hal.science/tel-00935257
Author Varrier, Sébastien
Maintainer CCSD
Last Updated May 7, 2026, 07:08 (UTC)
Created May 7, 2026, 07:08 (UTC)
Identifier NNT: 2013GRENT029
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Grenoble Images Parole Signal Automatique (GIPSA-lab) ; Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)
creator Varrier, Sébastien
date 2013-09-18T00:00:00
harvest_object_id 264590a1-e3c1-4a2e-994c-4b89667b91c3
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE