Demonstration

In leader-following formation, the leader plays a central role and is more vulnerable to malicious attacks. To effectively protect the leader, we investigate an event-based formation control problem of autonomous surface vehicles (ASVs) with multiple attackers and actuator failure. A novel adaptive formation keeping and interception scheme is developed for ASVs. In this scheme, a formation keeping controller and an interception controller are designed under a unified framework by sharing same event-triggered mechanism and fault-tolerant structure. To bridge these two controllers, a degree and distance driven formation interception (D3FI) strategy is designed by generating defenders. It is shown that with the proposed scheme, all attackers are intercepted without affecting formation keeping of remaining ASVs if the original topology is connected. Advantages of our scheme are: 1) stable controller switching is ensured during the formation cooperation and 2) the fault-tolerance is obtained with lower actuator updating frequencies and least number of adaptive parameters for each ASV.

With the rapid advancements of communication technology, distributed cooperative control has emerged as a promising approach, enabling participants to perform control based on their neighbouring agents, thereby facilitating a faster response and more flexibility. However, the privacy concerns must be addressed not only on the external adversaries but also on the internal adversaries, to encourage the participant to join this cooperative network. In contrast to existing literature, our study considers the scenario where participating agents are unaware of whether their neighbouring nodes inject noises, leading them to directly use the received data in control. We first design the noise injection scheme to ensure the mean-square consensus while preserving privacy in discrete-time multi-agent systems (MASs) and then derive the upper and lower bounds of the convergence rate. After that, we study the covariance matrix of the maximum likelihood estimate on the initial state of other agents based on the internal adversary's local information. The feasibility of (ε, δ)-differential privacy is characterized. Simulations illustrate the effectiveness of the Privacy-Preserving Cooperative Control (PPCC).