FDI and fault estimation based on differential evolution and analytical redundancy relations

Abstract

This article studies fault detection and isolation (FDI) and fault estimation in complex hybrid systems. The FDI approach is based on a set of unified constraints, called augmented Global Analytical Redundancy Relations (AGARRs), to detect and isolate the faults. In order to estimate the magnitude of the fault parameter in the fault candidates, a differential evolution (DE) method is employed. This developed method is applicable to estimation of multiple faults of parametric and nonparametric nature. Simulation is carried out to verify the effectiveness of the proposed method in a front steering system of a CyCab mobile robot with multiple faults.