论文标题
弹性平均共识：检测和补偿方法
Resilient Average Consensus: A Detection and Compensation Approach
论文作者
论文摘要
我们研究了具有不良节点的多代理系统的弹性平均共识问题。为了保护因行为不端节点影响的共识价值，我们通过检测不良行为来解决这个问题，减轻相应的不利影响并达到弹性平均共识。在本文中，考虑了一般类型的不端行为，包括欺骗攻击，意外故障和链接失败。我们通过两跳及通信信息以分布式方式表征了不良行为节点的不利影响，并开发了基于确定性检测能力的共识（D-DCC）算法，并具有衰减的耐断层误差绑定。考虑到由于链接失败而间歇性可用的情况，提出了一个随机扩展，提出了一个随机扩展名为随机检测，基于随机检测补偿的共识（S-DCC）算法。我们证明，D-DCC和S-DCC允许节点分别渐近地实现弹性的平衡，分别准确地和预期。然后，引入了Wasserstein距离来分析DCC的准确性。最后，进行大量模拟以验证所提出算法的有效性
We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus valuefrom being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered,including deception attacks, accidental faults and link failures. We characterize the adverse impact 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 information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus(S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient averageconsensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy ofS-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithm
