Resilient Average Consensus of Second-Order Multi-Agent Systems

In this paper, we study the problem of resilient average consensus for second-order multi-agent systems with misbehaving agents. General types of misbehaviors are considered, including false data injection attacks and accidental faults. The difficulties of this problem are to detect errors in a distributed way and accurately compensate two-dimension state errors by one-dimension acceleration input. We first provide sufficient conditions for second-order average consensus. Then, we design detection methods via two-hop communication information and propose schemes to compensate errors accurately in a distributed way inspired by sufficient conditions. Hence, we propose a finite input-errors detection-compensation-based consensus algorithm (FIDC). Considering infinite attacks on input, velocity and position, an extension named IADC is proposed with a fault-tolerance mechanism. We prove that the proposed algorithms allow agents to asymptotically achieve second-order average consensus. Finally, extensive simulations are conducted to verify the effectiveness of the algorithms.