Stochastic fault and cyber-attack detection and consensus control in multi-agent systems

In this paper, the stochastic fault and cyber-attack detection and consensus control problems are investigated for multi-agent systems. By using a Markovian approach, Linear Matrix Inequalities (LMI) are derived that incorporate relative information among the agents to detect stochastic faults and cyber-attacks and then resiliently control the system to reach a consensus. A mixed coding and Message Authentication approach is presented to detect data injection cyber-attacks on the communication links. By using the Bayesian inference, useful information regarding the cyber-attack, such as the probability of its occurrence, is derived. Simulation and two case study results corresponding to a team of multi-agent Autonomous Underwater Vehicles (UAVs) confirm and verify the effectiveness and capabilities of our proposed methodologies.

Automated summary

This paper investigates the stochastic fault and cyber-attack detection and consensus control problems for multi-agent systems, proposing an effective method that incorporates Markovian approach and Bayesian inference to detect and control stochastic faults and cyber-attacks in the system. Simulation and case study results confirm the effectiveness and capabilities of the proposed methodologies for a team of multi-agent Autonomous Underwater Vehicles (UAVs).