State and unknown input estimation via a proportional integral observer

This paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used in order to estimate the state and the faults which are assumed as unknown inputs. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models.

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