Fault detection and isolation of the attitude control subsystem of spacecraft formation flying using extended Kalman filters

In this paper, the problem of fault detection and isolation (FDI) of the attitude control subsystem (ACS) of spacecraft formation flying systems is considered. For developing the FDI schemes, an extended Kalman filter (EKF) is utilised which belongs to a class of nonlinear state estimation methods. Three architectures, namely centralised, decentralised, and semi-decentralised, are considered and the corresponding FDI strategies are designed and constructed. Appropriate residual generation techniques and threshold selection criteria are proposed for these architectures. The capabilities of the proposed architectures for accomplishing the FDI tasks are studied through extensive numerical simulations for a team of four satellites in formation flight. Using a confusion matrix evaluation criterion, it is shown that the centralised architecture can achieve the most reliable results relative to the semi-decentralised and decentralised architectures at the expense of availability of a centralised processing module that requires the entire team information set. On the other hand, the semi-decentralised performance is close to the centralised scheme without relying on the availability of the entire team information set. Furthermore, the results confirm that the FDI results in formations with angular velocity measurement sensors achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations that utilise attitude measurement sensors.