CAREER: Control Design for Dynamical Network Flows with Applications to Transportation

职业：动态网络流的控制设计及其在交通运输中的应用

• 批准号: 1454729
• 负责人: Ketan Savla
• 金额: $ 50万
• 依托单位: University of Southern California
• 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: 2015-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454729&HistoricalAwards=false
• 关键词: CAREER; Control; Design; Dynamical; Network

As our society is becoming more dependent on critical infrastructure networks such as transportation, communication, water, power, and gas, their efficient and resilient operation is becoming ever more important. Hence, one of the engineering grand challenges identified by the National Academy of Engineering is to restore and improve urban infrastructure systems. However, as recent evaluations by the American Society of Civil Engineers show, the state of our infrastructure systems continues to be substandard, and will likely remain so without massive investments. With significant growth in demand projected, a future infrastructure system that maintains the status quo will not function even at today's current, often inadequate, levels. It is increasingly being realized that leveraging sensing, actuation, and information technologies can achieve substantial improvements in the performance of these systems. These cyber technologies have the potential to endow our infrastructure networks with the capability to dynamically respond to changes in demand, supply, and even physical properties under disruptions. The existing approaches to control of infrastructure networks, however, are inadequate to realize the potential of this capability. This is because they are either heuristic with no formal performance guarantees; or they adopt static abstractions, and hence are useful only for long term planning; or the dynamical frameworks are used only for simulation and analysis purposes with little or no consideration for control design. The project will develop an integrated research and education program on rigorous control design for intelligent infrastructure networks, with a special emphasis on transportation. Collaborations with local transportation and planning agencies will facilitate rapid transition from research to practice. The outreach activities include development of an interactive traffic simulator, which would also serve as an experiment test bed to model dynamic driver behavior; and interactive network interdiction games to demonstrate the concepts of cascading failures and network robustness to general public. The education activities include development of new courses on analysis, control and estimation of infrastructure networks. Research opportunities will be expanded for undergraduates to implement control algorithms on professional transportation software to generate case studies, which will be used for our interactions with transportation agencies. Existing programs at the University of Southern California will be utilized to integrate inclusive teaching practices into educational activities in order to address retention of women, underrepresented and minority students.Network flow is a natural modeling paradigm for several infrastructure networks. The current state-of-the-art in theoretical network flow research primarily consists of algorithms for fast computation of maximum network flow capacity, or optimal flow distribution with respect to some performance metrics; and numerical analysis of dynamical network flows with cascade effects under fixed routing policies. The intellectual merits of the proposed research are: (i) a dynamical network flow framework that models coupling between dynamics of flow and jumps in network topology, e.g., due to cascading failures, as well as facilitates control design; (ii) new advancements in analysis for nonlinear dynamical systems using differential analysis and contraction principles, and applying them for analysis and control synthesis under proposed dynamical network flow; (iii) a computational framework for quantifying margins of resilience in terms of the disturbance generation process, network topology, and cascade dynamics; (iv) application of proposed tools to transportation through dynamic signal control, and inclusion of resilience metric in network design. The margin of resilience computations will identify canonical network flow concepts, besides the classical notion of cuts, as key indicators of network performance from efficiency and resilience perspective, under control and dynamical considerations. Beyond its immediate emphasis on dynamical network flows, the project aims to develop elements of robust control theory for networked dynamical systems.

随着我们的社会越来越依赖交通、通信、水、电力和天然气等关键基础设施网络，它们的高效和弹性运行变得越来越重要。因此，美国国家工程院确定的工程重大挑战之一是恢复和改善城市基础设施系统。然而，正如美国土木工程师协会最近的评估所示，我们的基础设施系统的状况仍然不合格，并且如果没有大规模投资，这种情况很可能会持续下去。随着预计需求的显着增长，维持现状的未来基础设施系统即使在当今通常不足的水平也将无法发挥作用。人们越来越认识到，利用传感、驱动和信息技术可以显着提高这些系统的性能。这些网络技术有潜力赋予我们的基础设施网络动态响应需求、供应甚至物理特性变化的能力。然而，现有的基础设施网络控制方法不足以实现这种能力的潜力。这是因为它们要么是启发式的，没有正式的性能保证；要么是启发式的。或者它们采用静态抽象，因此仅对长期规划有用；或者动态框架仅用于模拟和分析目的，很少或根本不考虑控制设计。该项目将开发一个关于智能基础设施网络严格控制设计的综合研究和教育计划，特别侧重于交通运输。与当地交通和规划机构的合作将促进从研究到实践的快速过渡。外展活动包括开发交互式交通模拟器，该模拟器也将作为模拟动态驾驶员行为的实验测试台；和交互式网络拦截游戏，向公众展示级联故障和网络稳健性的概念。教育活动包括开发有关基础设施网络分析、控制和估计的新课程。将扩大本科生的研究机会，在专业交通软件上实施控制算法以生成案例研究，这些案例研究将用于我们与交通机构的互动。南加州大学现有的项目将用于将包容性教学实践融入教育活动中，以解决女性、代表性不足和少数族裔学生的保留问题。网络流是多个基础设施网络的自然建模范例。当前理论网络流研究的最新技术主要包括快速计算最大网络流容量或针对某些性能指标的最佳流分配的算法；以及固定路由策略下具有级联效应的动态网络流的数值分析。所提出研究的智力优点是：（i）动态网络流框架，可以对流动态与网络拓扑跳跃之间的耦合进行建模，例如由于级联故障，并促进控制设计； (ii) 使用微分分析和收缩原理分析非线性动力系统的新进展，并将其应用于所提出的动态网络流下的分析和控制综合； (iii) 一个计算框架，用于量化扰动生成过程、网络拓扑和级联动力学方面的弹性裕度； (iv) 通过动态信号控制将所提出的工具应用于交通运输，并在网络设计中纳入弹性指标。除了经典的割断概念之外，弹性计算的边际还将确定规范的网络流概念，作为在控制和动态考虑下从效率和弹性角度来看网络性能的关键指标。除了直接强调动态网络流之外，该项目还旨在开发网络动态系统的鲁棒控制理论要素。