Quantifying Complex Behavior in Large-Scale Systems through Structured Uncertainty Analysis

通过结构化不确定性分析量化大型系统中的复杂行为

• 批准号: 1408442
• 负责人: Bassam Bamieh
• 金额: $ 48.53万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408442&HistoricalAwards=false
• 关键词: Quantifying; Complex; Behavior; Large; Scale

Quantifying Complex Behavior in Large-Scale Systems through Structured Uncertainty AnalysisLarge-scale systems of interacting dynamical elements are ubiquitous in nature and are increasingly common in engineered systems. Examples of such systems include large networks such as the power grid, vehicular traffic, social networks and many more. Such networks exhibit many complex phenomena arising from dynamic interaction between individual components. Qualitative understanding and quantitative prediction of these phenomena is a major current scientific and engineering challenge. This award supports research to develop quantitative analysis methods based on the techniques of structured stochastic uncertainty. These techniques model large-scale systems operating in uncertain environments, which include external forcing and attacks as well as internal disorder in interaction dynamics. The main observation is that even though individual uncertainty sources may be very small, their aggregate network effect may be large and even catastrophic, leading to loss of network stability or performance. Structured uncertainty analysis aims at quantifying exactly which type of small-scale uncertainty could lead to large-scale network-wide phenomena. Uncertainties in large-scale dynamic systems can occur in system parameters or structure as well as in uncertain forcing and disturbances. The theoretical aims of the research program are twofold. The first is to develop a unified approach to the seemingly distinct problems of large-scale networked systems on the one hand, and systems over continuum space described by partial differential equations on the other. These two sets of problems will both be treated as spatially distributed systems, but with discrete space being described by the network structure in the former, and continuum space in the latter. This theoretical synthesis enables a comparative development of results and methods in both settings in a manner similar to, but significantly richer, than analogies between discrete- and continuous-time systems. The second aim is to develop highly-scalable, simulation-free performance and stability tests for linear systems with both additive and multiplicative noise. Application areas for which specialized techniques will be developed are network/cooperative control where both the network structure and conditions are stochastically varying, and fluid flow with stochastic field coefficients. This methodological synthesis contributes to the foundations of Control Theory in the area of additive and multiplicative noise. It enables the solution of several problems in uncertain systems that are typically investigated through expensive stochastic simulations, and are therefore not scalable to large-scale systems. The ability to address discrete and continuum space in a unified manner will allow for a new synthesis of concepts and results between uncertain networks and systems described by partial differential equations.

通过结构不确定性分析量化大系统的复杂行为由相互作用的动力元素组成的大系统在自然界中普遍存在，并且在工程系统中越来越普遍。此类系统的例子包括电网、车辆交通、社交网络等大型网络。这种网络表现出许多复杂的现象，这些现象是由单个组件之间的动态相互作用引起的。对这些现象的定性理解和定量预测是当前科学和工程的主要挑战。该奖项支持基于结构化随机不确定性技术开发定量分析方法的研究。这些技术模拟在不确定环境中运行的大规模系统，包括外部强迫和攻击以及相互作用动力学中的内部混乱。主要观察结果是，尽管单个不确定性源可能很小，但它们的总体网络效应可能很大，甚至是灾难性的，导致网络稳定性或性能的丧失。结构化不确定性分析的目的是精确量化哪种类型的小规模不确定性可能导致大规模的网络范围现象。大尺度动态系统中的不确定性不仅存在于系统参数或结构中，也存在于不确定的强迫和扰动中。该研究计划的理论目的有两个。首先是开发一种统一的方法来解决一方面是大规模网络系统的看似不同的问题，另一方面是由偏微分方程描述的连续空间上的系统。这两组问题都将被视为空间分布系统，但前者用网络结构描述离散空间，后者用连续空间。这种理论综合使两种情况下的结果和方法的比较发展与离散和连续时间系统之间的类比相似，但要丰富得多。第二个目标是为具有加性和乘性噪声的线性系统开发高度可扩展，无需模拟的性能和稳定性测试。将发展专门技术的应用领域是网络结构和条件都是随机变化的网络/协同控制，以及具有随机场系数的流体流动。这种方法的综合为加性和乘性噪声领域的控制理论奠定了基础。它能够解决不确定系统中的几个问题，这些问题通常通过昂贵的随机模拟来研究，因此不能扩展到大规模系统。以统一的方式处理离散和连续空间的能力将允许在不确定网络和偏微分方程描述的系统之间进行概念和结果的新综合。