Reconfiguration and Cooperative Control for Multi-Agent Networks

多智能体网络的重构与协作控制

• 批准号: RGPIN-2017-06964
• 负责人: Aghdam, Amir
• 金额: $ 4.23万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752015
• 关键词: Reconfiguration; Cooperative; Control; Multi; Agent

There is an increasing interest in the deployment of multi-agent networks for emerging applications such as surveillance and target tracking. This proposal focuses on two research problems in the area of multi-agent networks. The first problem concerns network reconfiguration, which aims at improving the performance of a network by properly positioning its nodes and/or changing the weights of its links (e.g., by adjusting the communication/sensing power of the nodes). This is a problem which requires a strong practical background, and while the results will be developed for a general class of asymmetric networks, the applicant's prior experience in the design of experimentally validated distributed control schemes for underwater sensor networks puts him in a unique position to address some of the practical shortcomings of existing results for this type of systems. One of the main characteristics of asymmetric networks is that the graph representing them is directed, and in the case of underwater sensor networks, particularly, it is random too. However, there are not many results in the literature for the analysis of this type of graphs, due to their complexity compared to (deterministic) undirected graphs. This limits the extent of which certain observations in such networks can be justified analytically. For example, it is known that the relative positions of the acoustic nodes in an underwater sensor network can have a significant impact on data aggregation performance. In fact, similar observations have been reported in other types of asymmetric networks with no convincing theoretical justification. The applicant and his team have recently developed some theoretical results, validated by simulations, that relate the important properties of general weighted directed graphs (such as connectivity) to the configuration of the network, enabling the research community for the first time to justify these observations theoretically, and more importantly, use the results to further improve the performance of the network. These results will be used in the proposed research to find the optimal configuration for asymmetric networks. The results can also be used in other applications such as traffic network control systems, to justify some counter-intuitive observations reported in this type of systems (e.g., negative impact of the addition of some roads on the overall traffic flow in the network). The other problem investigated in this proposal is concerned with cooperative decision making for heading control of multiple vehicles, where it is desired to coordinate a group of vehicles in order to detect, localize, track and intercept a group of objects that arrive in a protected area (mission space) at random time instants. The proposed dynamic decision making and control design is based on a reward allocation strategy which directs the vehicles toward the objects in an optimal cooperative fashion.

在监视和目标跟踪等新兴应用中部署多代理网络的兴趣越来越大。该方案重点研究了多智能体网络领域的两个研究问题。第一个问题涉及网络重新配置，其目的是通过适当地定位其节点和/或改变其链路的权重(例如，通过调整节点的通信/感测功率)来提高网络的性能。这是一个需要很强的实践背景的问题，虽然结果将针对一般类型的非对称网络进行开发，但申请人在设计用于水下传感器网络的实验验证的分布式控制方案方面的先前经验使他处于一个独特的位置，可以解决此类系统现有结果的一些实际缺陷。非对称网络的一个主要特征是表示它们的图是有向的，特别是在水下传感器网络的情况下，它也是随机的。然而，由于与(确定性的)无向图相比，这类图的复杂性，文献中对这类图的分析结果并不多。这限制了这类网络中的某些观测在多大程度上可以从分析上得到证明。例如，众所周知，水下传感器网络中水声节点的相对位置会对数据聚合性能产生重大影响。事实上，在没有令人信服的理论证明的情况下，在其他类型的不对称网络中也有类似的观察结果。申请人和他的团队最近开发了一些理论结果，并通过模拟进行了验证，这些结果将一般加权有向图的重要属性(如连通性)与网络的配置相关联，使研究界首次能够从理论上证明这些观察结果的合理性，更重要的是，使用这些结果来进一步提高网络的性能。这些结果将被用于拟议的研究中，以找到非对称网络的最优配置。该结果也可用于其他应用，例如交通网络控制系统，以证明在这类系统中报告的一些违反直觉的观察结果(例如，某些道路的增加对网络中的整体交通流的负面影响)。该方案中研究的另一个问题涉及多车辆航向控制的协作决策，其中希望协调一组车辆以检测、定位、跟踪和拦截在随机时刻到达保护区(任务空间)的一组对象。所提出的动态决策与控制设计基于报酬分配策略，以最优协作方式引导车辆朝向目标。