Sensor fault estimation for nonlinear systems described by multiple models

Atef Kheder (1) , Kamel Ben Othman (1) , Didier Maquin (2) , Mohamed Benrejeb (1)

1 LR-Automatique-ENIT - Laboratoire d'Automatique [Tunis]
2 CRAN - Centre de Recherche en Automatique de Nancy

This paper deals with the problem of sensor fault estimation for linear and nonlinear systems. Thanks to the introduction of an augmented generalized state vector, including the original state vector and a filtered output of the system, the sensor fault ap- pears as an unknown input. Therefore, an adaptive proportional integral observer is used to estimate simultaneously the state of the system and the unknown input. In order to provide some robustness properties, the disturbance effect on the state and fault estimation errors is minimized in an L2 framework. State and sensor fault estimation is firstly presented for linear systems and is next extended to nonlinear systems described by Takagi- Sugeno models.

Keywords: State estimation; Takagi-Sugeno model; Sensor fault estimation; Unknown input; Multiple model; Proportional integral observer

Domains: Automatic

Atef Kheder, Kamel Ben Othman, Didier Maquin, Mohamed Benrejeb. Sensor fault estimation for nonlinear systems described by multiple models. Transactions on Systems, Signals & Devices, 2011, 6 (1), pp.49-66. ⟨hal-00548129⟩