# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
### Model-Based Methods
[PERSON] and [PERSON] proposed an innovative FDI strategy for the ACS in SFF [77]. This method can use three different setups: decentralized, centralized, and semi-decentralized (distributed). As a result of these three configurations, a fault diagnostic system can be created for multisatellite systems. Compared with the other two FDI architectures, the centralized FDI architecture is more effective in missions incorporating angular velocity sensors. However, it also generates more false alarms. On the other hand, FDI decentralized architectures announce fault occurrences more frequently and produce fewer false alarms. The centralized Fault Detection (FD) architecture performs better in FDI, resulting in fewer false alarms in missions when formation flight integrates attitude-measuring sensors than the other two systems. Furthermore, the decentralized FD architecture communicates fault occurrences less frequently while producing fewer false alarms compared with the distributed FD architecture. An \(H_{\infty}\)-based robust distributed observer to synchronize the orientations of several satellites is investigated in [78]. A powerful distributed \(H_{\infty}\) is established at each satellite to detect intermittent faults. To calculate the gains for observers, they designed two sets of Linear Matrix Inequality (LMI) criteria. Numerous satellite numerical simulations show that the resilient \(H_{\infty}\) observer effectively estimates time-varying and intermittent faults when the satellite's communication layout is an undirected graph. Additionally, the designed observer is shown to be effective when several satellites have problems at once. In [58], [PERSON] and [PERSON] introduced a novel approach involving intersatellite measurements, considering relative positional data and orbital parameters. Their focus was on FDI within spacecraft rate gyroscopes. This innovative technique [PERSON] a consistent motion characteristic, specifically restricting dynamic states during relative motion. Consequently, the primary satellite's angular velocity vector aligns with a quadratic surface. This ascertained motion characteristic aids in [PERSON] faults within the gyroscope system and providing a rough estimation of the relevant scale factor or bias for the rate gyroscopes associated with the primary satellite. The proposed approach eliminates the need for additional subsystems within the SFF framework. An exploration concerns a comprehensive approach for FDI within a specific class of nonlinear SFF systems [67]. The methodology considers model uncertainties, input variations, and environmental disturbances throughout the diagnostic process. To address fault detection, a nonlinear observer is carefully engineered to minimize uncertainty within the robust \(H_{\infty}\) framework. The observer gain matrices are computed using an LMI formulation. Robust UIO are developed to pinpoint the faulty actuator and facilitate fault isolation. This isolation is executed through a strategic implementation of generalized observer techniques. The novel observer architecture can concurrently estimate faults and states while effectively mitigating the impact of unknown input disturbances, model uncertainties, and external disturbances. This is achieved by employing Lipschitz formulations and the Linear Parameter Varying (LPV) method across all proposed observers, resulting in less conservative LMI conditions. It is essential to mention that this approach allows each satellite to diagnose its faults and those of its adjacent satellites. [PERSON] and [PERSON] [59] introduced an approach to estimate faults and ensure fault tolerance in the control system for nonlinear SFF systems, explicitly addressing actuator faults. A decentralized UIO is designed to estimate the actuator fault factors. The estimated fault values obtained are then utilized to design a distributed fault-tolerant controller for SFF.