Actuator fault detection and isolation of nonlinear systems by robust fuzzy observers

Document Type : Original Article

Authors

Laboratoire Automatique et Signaux Annaba (LASA) University of Badji Mokhtar PO. Box 12, 23000 Annaba Algeria.

Abstract

Abstract:
This paper presents a model-based technique for fault detection and isolation (FDI) of
actuators of a benchmark which schematizes a hydraulic process made up of three tanks.
Takagi Sugeno’s model approach is used for describing the dynamic of the system. In
the same way, the fuzzy membership functions used for constructing Takagi and
Sugeno’s model are combined with local unknown input observers to form robust fuzzy
observer. Sufficient conditions for the existence of this fuzzy observer are derived. The
stability as well as eigen-value constraints conditions are presented and solved in the
LMI framework. For the observer gives a good estimation without amplifying noise and
with a convergence faster than the dynamic of the system a eigen-value assignment is
necessary. Robust residual signals, generated by these fuzzy observers robust to
unknown inputs are dedicated to supervise actuators. These residuals are sensitive to
faults acting on one actuator and are also insensitive to faults on the others by
considering faults such unknown disturbances. This permits to carry out directly the
isolation of the faulty actuator.

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