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Data-guided Control: Fundamental Limits in Presence of Nonlinearities, Streaming Data, and Networks

数据引导控制：非线性、流数据和网络存在的基本限制

基本信息

批准号：
2149470
负责人：
Mehran Mesbahi
金额：
$ 40万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149470&HistoricalAwards=false
关键词：
Data guided Control Fundamental Limits

项目摘要

Data-guided control is an emerging area in system sciences that aims to embrace time series data as its core construct for control of dynamic systems–replacing or complementing their models. This project addresses foundational gaps in the current state of the art in data-guided control. The approach is multi-pronged; on one hand, the current paradigm is extended by facilitating its applications in the context of nonlinear systems and trajectory funnels, as well as large-scale networked systems. On the other hand, foundational system-theoretic constructs for control with online streaming data are developed. The project also has a significant educational component. The PI will develop and re-design a number of control courses at the University of Washington, providing a complementary “data-model” perspective on dynamic systems. These courses will encourage students with interest in machine learning to appreciate the theoretical underpinnings of model-based design. In parallel, the educational goals of this project involve inspiring students with interest in systems and control theory to re-examine data-guided analysis from a perspective that is rooted in system theory, yet embraces how data, statistics, and optimization significantly complement the more traditional training in systems and control. This project contributes to making system-theoretic concepts in areas such complex networks, infrastructure systems, and health care, as relevant and useful as data analytic tools and methods. Also envisioned is reviving a deeper appreciation for systems and control in students who have gravitated towards machine learning in the past decade. This new perspective also facilitates attracting a new cohort of students to systems by broadening its scope to realms where first-principle models are neither available nor justifiable. The project will develop novel data-parameterized analysis and synthesis techniques for dynamic systems. Building on the notions of informativity and Willem’s Fundamental Lemma, the use of data-parameterized matrix inequalities is examined in the presence of disturbances and for funnel synthesis in nonlinear trajectory-following. Next, scaling laws will be examined that clarify the relation between suboptimality measures and analytic properties of design objectives on one hand, and data-snapshots required for analysis and synthesis on the other. The project will then examine new system-theoretic notions for control synthesis motivated by streaming data, as well as rigorously identify the role of data-reduction techniques for the control of networked and multi-agent systems. The overarching goal of the project is developing data-guided system-theoretic techniques that transparently capture the “duality” between models and data in online control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据引导控制是系统科学中的一个新兴领域，其目标是将时间序列数据作为其控制动态系统的核心结构-取代或补充其模型。该项目解决了数据引导控制的当前最新技术中的基本差距。这一办法是多管齐下的;一方面，通过促进其在非线性系统和轨迹漏斗以及大规模网络化系统的背景下的应用，扩展了当前的范例。另一方面，开发了用于在线流数据控制的基本系统理论结构。该项目还具有重要的教育内容。PI将在华盛顿大学开发和重新设计一些控制课程，为动态系统提供补充的“数据模型”视角。这些课程将鼓励对机器学习感兴趣的学生欣赏基于模型的设计的理论基础。与此同时，该项目的教育目标涉及激发学生对系统和控制理论的兴趣，从植根于系统理论的角度重新审视数据引导分析，但包括数据，统计和优化如何显着补充系统和控制的传统培训。该项目有助于使系统理论概念在复杂网络，基础设施系统和医疗保健等领域与数据分析工具和方法一样相关和有用。还设想恢复对系统和控制的更深层次的欣赏，在过去十年中，学生们被机器学习所吸引。这种新的视角也有助于吸引新的学生群体的系统，扩大其范围的领域，第一原理模型既不可用，也不合理。该项目将为动态系统开发新的数据参数化分析和综合技术。信息性和威廉的基本引理的概念的基础上，使用数据参数化的矩阵不等式检查存在的干扰和漏斗合成在非线性随机跟随。接下来，将研究标度律，一方面阐明次优性度量和设计目标的分析性质之间的关系，另一方面阐明分析和综合所需的数据快照。然后，该项目将研究由流数据驱动的控制合成的新系统理论概念，并严格确定数据简化技术在网络和多智能体系统控制中的作用。该项目的总体目标是开发数据引导的系统理论技术，透明地捕捉在线控制中模型和数据之间的“二元性”。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。