4.7 Article

Secure Sampled-Data Consensus of Multi-Agent Systems Under Asynchronous Deception Attacks With Application to Unmanned Surface Vehicles

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2023.3330995

Keywords

Secure sampled-data consensus; multiagent systems; asynchronous deception attacks; Lyapunov-Krasovskii functional

Ask authors/readers for more resources

This paper studies the secure sampled-data consensus of multi-agent systems (MASs) under asynchronous independent deception attacks and multiplicative control gain disturbances. It proposes a looped Lyapunov-Krasovskii functional (LKF) approach and a coupled memory-based sampled-data control scheme to ensure the system's performance.
This paper studies the secure sampled-data consensus of multi-agent systems (MASs) under asynchronous independent deception attacks and multiplicative control gain disturbances by developing the looped Lyapunov-Krasovskii functional (LKF) approach. Because of the openness and shareability of communication network, as well as the time delay in the process of data transmission, two independent deception attacks with asynchronous phenomenon are considered in the sensor-to-controller (SC) and controller-to-actuator (CA) channel of MAS communication network. Furthermore, a coupled memory-based sampled-data control scheme is proposed and fractional-delay states on sampling intervals are introduced to construct an improved delay-dependent looped LKF. Therefore, a relaxed condition with lower conservatism is established to asymptotically ensure the secure sampled-data consensus of MASs in mean square under our designed control protocol. Finally, a simulation test applied to unmanned surface vehicles is conducted to validated the performance of designed control strategy.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available