Fault Detection Filtering for Unmanned Surface Vehicles with Markov Switching and Random Packet Losses

This paper explores the dissipative fault detection filtering (FDF) issue for unmanned surface vehicles (USVs) within a network environment, considering switching channels and random occurrence of packet losses. Specifically, a multi-channel transmission mechanism is adopted to enhance the reliability of the system, and the switching channel is orchestrated by a Markov chain. The random intermittent data dropouts in each channel between the system and FDF modeled as a Bernoulli process are considered. Leveraging the Lyapunov theory and slack matrix technique, switching-channel-dependent FDF is crafted to detect faults for USVs and ensure that the augmented system keeps stochastic stability while attaining a strictly dissipative performance. Finally, an illustrative example is presented to validate the effectiveness of the proposed method for USVs.