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This paper introduces the particle-filtering (PF) based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution...
This paper addresses the problem of fault detection and isolation (FDI) for a class of Lipschitz nonlinear systems with unknown inputs. We propose two new approaches based on unknown input observer design technique to detect, locate and estimate additional faults. The observer design problem is formulated in Linear Matrix Inequalities (LMI) terms which can be solved easily using LMI technique. An...
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