Title :

Sensor and actuator fault detection and isolation using two model based approaches: Application to an autonomous electric vehicle

Author(s) :

Bouibed, Kamel [Auteur]
Aitouche, Abdel [Auteur correspondant]
Systèmes Tolérants aux Fautes [STF]
Merchez, Mireille [Auteur]
Systèmes Tolérants aux Fautes [STF]

Conference title :

Conference on Control & Automation (MED), 2010 18th Mediterranean

City :

Marrakech

Country :

Maroc

Start date of the conference :

2010-06-23

Book title :

Conference on Control & Automation (MED), 2010 18th Mediterranean

Publication date :

2010-06-25

HAL domain(s) :

Sciences de l'ingénieur [physics]/Automatique / Robotique

English abstract : [en]

In this paper, two model based approaches are proposed in order to detect and to isolate sensor and actuator faults on an electric autonomous vehicle. The first one is based on sliding mode observers. The principle is to ...
Show more >In this paper, two model based approaches are proposed in order to detect and to isolate sensor and actuator faults on an electric autonomous vehicle. The first one is based on sliding mode observers. The principle is to reconstruct the state vector and the outputs of the system by sliding mode observers and to compare the estimated outputs with those measured, the obtained difference is considered as residual. The second approach rests on nonlinear analytical redundancy (NLAR) and consists to eliminate the unknown states and variables in order to obtain relations where all variables are known. The objective of this paper is to show the interest of the two approaches for detecting sensor or actuator faults of autonomous electric vehicle. Simulation results show that the method of NLAR is more adequate to detect actuator faults and the sliding mode observer is better for detecting sensor faults.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL