Accurate Resilient Average Consensus via Detection and Compensation

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. Different from the widely investigated Mean-Subsequence-Reduced-based and detection-isolation-based approaches which guarantee consensus, in this paper, we address this problem by detecting misbehaviors, mitigating corresponding impact and achieving accurate average consensus. General types of misbehaviors are considered, including deception attacks and accidental faults. We characterize the disturbances of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where such information is intermittently available, a stochastic extension named S-DCC is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve average consensus exactly and in expectation, respectively. Finally, extensive simulations verify the effectiveness of the proposed algorithms.