# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
### Model-Based Methods
Subsequently, an algorithm for fault-tolerant formation control is developed, using Adaptive Terminal Sliding Mode Control (ATSMC) techniques to improve the synchronization of each follower spacecraft with the leader spacecraft, even when faced with actuator faults. In another research, [PERSON] and [PERSON] [60] undertook to design a robust UIO specifically crafted to estimate faults within clusters of small satellites. Operating within clusters, these satellites present heterogeneous dynamics characterized by significant diversity owing to environmental conditions and inherent nonlinearities. The study extends its focus beyond fault estimation, investigating that the designed observer and controller logic not only estimate faults accurately but also arrange synchronized behavior among satellites within each cluster. This synchronization shows as a state of consensus, steering the satellites towards a collective achievement of predefined objectives and trajectories designed for their mission. In a separate study, [PERSON] and [PERSON] [61] investigated the problem of fault diagnosis within clusters of small satellites. These satellites operate during diverse environmental conditions, presenting a difficult challenge in mitigating both disturbances and faults while maintaining the desired formation state among the satellites. The study aimed to alleviate the adverse effects of disturbances through an elaborate fault diagnosis approach, ultimately ensuring a consistent and reliable formation state among the small satellites, even in dynamically changing environments. To fortify the observer's resilience against external disturbances as outlined in [PERSON] and [PERSON]'s work [60; 61], they employed the \(H_{\infty}\) approach. In a separate study conducted by the same authors, a dissipativity-based UIO is implemented for the Lipschitz nonlinear multisatellite systems. This strategy is designed to mitigate the impact of disturbances across two scenarios: one where matching conditions are met and another where they are not [62]. [PERSON] et al. [51] aimed to detect cyber-threats during UAV Formation Flight (UFF). [PERSON] et al. have adopted a decentralized approach using UIO for FDI, which is particularly suited to the distinctive attributes of UFF and their associated control algorithms, which may involve unknown parameters. This approach facilitates the identification of system faults and extends its utility to detecting cyber intrusions and precisely locating compromised UAVs. The primary objective of this study is to introduce an algorithm capable of identifying compromised UAVs without compromising the overall performance of the formation. Numerical simulations conducted by the authors provide evidence that diagnosing cyber intrusions within a hexagonal UAV formation is highly effective [51]. [PERSON] et al. [49] proposed a unique solution to the problem of implementing UAVs in environments subject to significant disturbances. The FDI method was developed to integrate a continuous-time residual generator with a fault diagnosis system using a DES. Specifically, their hybrid FDI algorithm is applied to detect and isolate actuator faults in a quad-rotor network to demonstrate its efficacy. [PERSON] and [PERSON] [50] explored investigating, designing, and examining FDI filters for actuator systems within a network of aerial and space unmanned vehicles. The study emphasizes the actuator fault patterns within the team of agents, characterizing it as an excessively actuated system. An insolubility measure is introduced to evaluate fault patterns and design a novel set of structured residuals. This set is engineered to perform detection selectively and accurately, as well as isolate multiple faults that have been identified, particularly those with dependent fault patterns, such as excessively actuated systems. Based on these concepts, their algorithm is implemented to tackle the actuator FDI challenge in unmanned vehicle networks operating under different architectures, including centralized, distributed, and decentralized.