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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637399
• 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.

人们对多代理网络的部署越来越感兴趣，这些网络用于监视和目标跟踪等新兴应用。本文主要研究了多智能体网络中的两个研究问题。第一个问题涉及网络重构，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/传感功率）来改善网络的性能。这是一个需要强大实践背景的问题，虽然结果将用于一般类型的非对称网络，但申请人在水下传感器网络的实验验证分布式控制方案设计方面的先前经验使他处于一个独特的位置，可以解决这种类型系统现有结果的一些实际缺点。非对称网络的主要特征之一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）无向图相比，这类图的复杂性，在文献中没有很多结果用于分析这类图。这就限制了这种网络中某些观测结果的分析正当性。例如，众所周知，水下传感器网络中声学节点的相对位置会对数据聚合性能产生重大影响。事实上，在其他类型的不对称网络中也有类似的观察结果，但没有令人信服的理论依据。申请人和他的团队最近开发了一些理论结果，通过模拟验证，将一般加权有向图的重要属性（如连通性）与网络的配置联系起来，使研究界首次从理论上证明这些观察结果，更重要的是，使用这些结果进一步提高网络的性能。这些结果将用于研究非对称网络的最优配置。结果也可以用于其他应用，如交通网络控制系统，以证明在这类系统中报告的一些反直觉的观察结果（例如，增加一些道路对网络中整体交通流量的负面影响）。本文研究的另一个问题是多车辆航向控制的协同决策问题，该问题要求协调一组车辆，以便在任意时刻探测、定位、跟踪和拦截到达保护区域（任务空间）的一组物体。提出的动态决策与控制设计基于奖励分配策略，该策略以最优合作方式引导车辆向目标移动。