论文标题
通过状态分解保护隐私的动态平均共识：多机器人形成控制的案例研究
Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control
论文作者
论文摘要
动态平均共识是一个分散的控制/估计框架，其中一组代理会协同跟踪局部时间变化的参考信号的平均值。在本文中，我们开发了一种基于国家分解的新型隐私保护计划，以保护代理商与邻近代理商共享信息时的隐私。具体而言，我们首先表明外部窃听器可以成功窃听常规动态平均共识算法中所有代理的参考信号。为了保护隐私免受窃听者的影响，开发了一个状态分解方案，其中每个代理的原始状态被分解为两个子态：一个人在节点间相互作用中取代了原始状态的作用，而另一个子状态仅与第一个国家进行通信，并且与其他邻近的代理人看不见。进行严格的分析以表明1）提议的隐私方案保留了平均共识的融合； 2）保护代理的隐私受到保护，以使窃听者无法以任何保证的准确性发现私人参考信号。然后将发达隐私的动态平均共识框架应用于多个非全面移动机器人的形成控制，其中证明了该方案的功效。提供数值模拟以说明所提出方法的有效性。
Dynamic average consensus is a decentralized control/estimation framework where a group of agents cooperatively track the average of local time-varying reference signals. In this paper, we develop a novel state decomposition-based privacy preservation scheme to protect the privacy of agents when sharing information with neighboring agents. Specifically, we first show that an external eavesdropper can successfully wiretap the reference signals of all agents in a conventional dynamic average consensus algorithm. To protect privacy against the eavesdropper, a state decomposition scheme is developed where the original state of each agent is decomposed into two sub-states: one succeeds the role of the original state in inter-node interactions, while the other sub-state only communicates with the first one and is invisible to other neighboring agents. Rigorous analyses are performed to show that 1) the proposed privacy scheme preserves the convergence of the average consensus; and 2) the privacy of the agents is protected such that an eavesdropper cannot discover the private reference signals with any guaranteed accuracy. The developed privacy-preserving dynamic average consensus framework is then applied to the formation control of multiple non-holonomic mobile robots, in which the efficacy of the scheme is demonstrated. Numerical simulation is provided to illustrate the effectiveness of the proposed approach.
