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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=643541
• 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.

有越来越多的兴趣在部署多智能体网络的新兴应用，如监视和目标跟踪。该建议主要集中在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，其目的在于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）。这是一个问题，需要一个强大的实际背景，而结果将开发为一般类的非对称网络，申请人的经验，在设计实验验证的分布式控制方案的水下传感器网络使他在一个独特的位置，以解决一些实际的缺点，现有的结果，这种类型的系统。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，在文献中没有太多的结果，这种类型的图的分析，由于其复杂性相比（确定性）无向图。这限制了在这种网络中的某些观察可以通过分析来证明的程度。例如，已知水下传感器网络中声学节点的相对位置可以对数据聚合性能具有显著影响。事实上，在其他类型的非对称网络中也报告了类似的观察结果，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。这些结果将用于所提出的研究，以找到最佳的配置为非对称网络。这些结果也可以用于其他应用，如交通网络控制系统，以证明在这种类型的系统中报告的一些反直观的观察结果（例如，增加一些道路对网络中整体交通流量的负面影响）。在这个建议中调查的另一个问题是有关的合作决策的航向控制的多个车辆，它是希望协调一组车辆，以检测，定位，跟踪和拦截的一组对象，在一个保护区（使命空间）在随机的时刻到达。建议的动态决策和控制设计是基于奖励分配策略，该策略以最佳的合作方式将车辆引导到目标。