Asynchronous Fault Detection for Unmanned Marine Vehicles Under False Data Injection attacks
TL;DR: This paper addresses the challenge of thruster fault detection (FD) in Unmanned Marine Vehicles (UMVs) under the threat of False Data Injection (FDI) attacks.
Abstract: This paper addresses the challenge of thruster fault detection (FD) in Unmanned Marine Vehicles (UMVs) under the threat of False Data Injection (FDI) attacks. FDI attacks can corrupt sensor data, leading to erroneous fault detection and impaired system performance. To tackle this issue, the study proposes a robust detection framework that integrates an advanced asynchronous switched filter specifically designed to counteract the effects of FDI attacks. The proposed framework employs a combination of adaptive filtering techniques and model-based approaches to effectively identify and isolate faults despite the presence of manipulated data. The analysis utilizes a model-dependent average dwell time (MDADT) and piecewise Lyapunov functions (PLF) to establish stability and performance criteria under the influence of FDI attacks. Additionally, the study derives conditions for filter design and performance guarantees, ensuring accurate fault detection even in the presence of data corruption. Simulation results demonstrate the framework's efficacy in maintaining high detection accuracy and system reliability. The findings underscore the importance of incorporating adaptive and resilient strategies to enhance fault detection capabilities in the face of sophisticated cyber threats.
Submission Number: 248
Loading