A Hierarchical Architecture for Cooperative Actuator Fault Estimation and Accommodation of Formation Flying Satellites in Deep Space

A new cooperative fault accommodation algorithm based on a multi-level hierarchical architecture is proposed for satellite formation flying missions. This framework introduces a high-level (HL) supervisor and two recovery modules, namely a low-level fault recovery (LLFR) module and a formation-level fault recovery (FLFR) module. At the LLFR module, a new hybrid and switching framework is proposed for cooperative actuator fault estimation of formation flying satellites in deep space. The formation states are distributed among local detection and estimation filters. Each system mode represents a certain cooperative estimation scheme and communication topology among local estimation filters. The mode transitions represent the reconfiguration of the estimation schemes, where the transitions are governed by information that is provided by the detection filters. It is shown that our proposed hybrid and switching framework confines the effects of unmodeled dynamics, disturbances, and uncertainties to local parameter estimators, thereby preventing the propagation of inaccurate information to other estimation filters. Moreover, at the LLFR module a conventional recovery controller is implemented by using estimates of the fault severities. Due to an imprecise fault estimate and an ineffective recovery controller, the HL supervisor detects violation of the mission error specifications. The FLFR module is then activated to compensate for the performance degradations of the faulty satellite by requiring that the healthy satellites allocate additional resources to remedy the problem. Consequently, fault is cooperatively recovered by our proposed architecture, and the formation flying mission specifications are satisfied. Simulation results confirm the validity and effectiveness of our developed and proposed analytical work

Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying using Extended Kalman Filters

In this thesis, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. For this purpose, first the attitude dynamics of a single spacecraft is analyzed and a nonlinear model is defined for our problem. This is followed up by generating the model of the spacecraft formation flight using the attitude model and controlling the formation based on virtual structure control scheme. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on extended Kalman filters. Moreover, the `residual generation and threshold selection techniques are proposed for these architectures. The capabilities of the architectures for fault detection are studied through extensive numerical simulations. Using a confusion matrix evaluation system, it is shown that the centralized architecture can achieve the most reliable results relative to the semi-decentralized and decentralized architectures. Furthermore, the results confirm that the fault detection in formations with angular velocity measurements achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations with only attitude measurements. In order to isolate the faults, structured residuals are designed for the decentralized, semi-decentralized, and centralized architectures. By using the confusion matrix tables, the results from each isolation technique are presented for different fault scenarios. Finally, based on the comparisons made among the architectures, it is shown that the centralized architecture has the highest accuracy in isolating the faults in the formations. Furthermore, the results confirm that fault isolation in formations with angular velocity measurements achieve higher level of accuracy when compared to formations with only attitude measurements