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.

分布式动态系统执行的许多任务可以被认为是发生在由通信链路互连的动态节点的网络上。这些任务包括分布式移动的代理（如车辆）的传感、估计、控制和优化。这些任务的实现通常简化为在每个网络节点处定义的某个变量的平均值或加权平均值的计算。由于跨网络链路的通信可能会很慢或很昂贵，因此将计算此平均值的工作分散到所有联网系统中非常重要。这促使了“分布式算法”的发展，这种算法只依赖于来自近邻的信息，也就是说，来自可以直接相互通信的系统的信息。虽然分布式平均算法已经取得了很大的进展，但这些算法依赖于高度简化的假设。该项目将在一系列现实系统上实现有效的分布平均，并在机器人网络上实验验证结果。现有的分布式平均方法主要依赖于线性局部重复平均型或共识型算法。这些只能处理规定的情况，如平均初始条件，或稳定的信号，或拉普拉斯变换量。因此，它们对实际应用的适用性是有限的。本计画的目标是基于新颖的非平滑非线性演算法，推导出一个强健的分散式平均追踪架构，以使网路系统的分散式协调成为可能。该项目将解决稳健的分布式平均跟踪占测量和通信噪声，不同的代理人的不同的部分可观测性和不一致的数据质量，固有的物理动力学和优化目标。研究结果将填补分布式平均范例中的差距，使许多涉及网络系统的民用、国土安全和军事应用受益。