Robust Distributed Average Tracking for Networked Systems

网络系统的鲁棒分布式平均跟踪

• 批准号: 1537729
• 负责人: Wei Ren
• 金额: $ 23.88万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537729&HistoricalAwards=false
• 关键词: Robust; Distributed; Average; Tracking; Networked

Many tasks performed by distributed dynamic systems can be thought of as occurring on a network of dynamic nodes interconnected by communication links. These tasks include sensing, estimation, control, and optimization by distributed mobile agents, such as vehicles. Implementation of these tasks often reduces to computation of the average, or weighted average, of some variable defined at each network node. Because communication across network links may be slow or expensive, it is important to spread the effort of computing this average across all the networked systems. This motivates the development of "distributed algorithms" that rely only on information from immediate neighbors, that is, from systems that can directly communicate with each other. While great progress has been made in distributed averaging algorithms, these rely on highly simplifying assumptions. This project will enable effective distributing averaging on a range of realistic systems, and experimentally validate the results on a network of robots. The result will apply to numerous open, physically relevant, problems.Existing distributed averaging methods rely primarily on linear local repeated averaging-type or consensus-type algorithms. These can only deal with prescribed cases, such as averaging initial conditions, or steady signals, or Laplace transformed quantities. Hence their applicability to practical applications is limited. The objective of this project is to derive a robust distributed average tracking framework based on novel nonsmooth nonlinear algorithms to enable distributed coordination of networked systems. The project will address robust distributed average tracking accounting for measurement and communication noise, different agents' varying partial observability and discrepant data quality, inherent physical dynamics, and optimization objectives. The results will fill in the gap in the distributed averaging paradigm to benefit many civilian, homeland security, and military applications involving networked systems.

通过分布式动态系统执行的许多任务可以被认为是在通过通信链接互连的动态节点网络上发生的。这些任务包括分布式移动试剂（例如车辆）的传感，估计，控制和优化。这些任务的实现通常会减少到每个网络节点上定义的某些变量的平均平均值或加权平均值的计算。由于跨网络链接的通信可能很慢或昂贵，因此在所有网络系统中分配计算该平均值的努力很重要。这激发了仅依赖于直接邻居的信息的“分布式算法”的开发，也就是说，从可以直接互相通信的系统中。尽管在分布式平均算法中取得了巨大进展，但这些取决于高度简化的假设。该项目将在一系列现实的系统上有效分发平均，并在机器人网络上实验验证结果。结果将适用于许多开放，物理相关的问题。存在分布式平均方法主要依赖于线性局部重复的平均型或共识型算法。这些只能处理规定的案例，例如平均初始条件，稳定的信号或拉普拉斯转换数量。因此，它们对实际应用的适用性是有限的。该项目的目的是基于新颖的非平滑非线性算法来得出强大的分布式平均跟踪框架，以实现网络系统的分布式协调。该项目将解决鲁棒的分布式跟踪核算，以计量和通信噪声，不同的代理的不同部分可观察性以及差异数据质量，固有的物理动态和优化目标。结果将填补分布式平均范式的空白，以使许多平民，国土安全和涉及网络系统的军事应用受益。