CPS: Synergy: Collaborative Research: Event-Based Information Acquisition, Learning, and Control in High-Dimensional Cyber-Physical Systems

CPS：协同：协作研究：高维网络物理系统中基于事件的信息获取、学习和控制

• 批准号: 1329936
• 负责人: Bruno Sinopoli
• 金额: $ 33.33万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1329936&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Event

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems. In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing.The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored.In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

本项目主要研究网络物理系统中的信息获取、状态估计和控制问题。在我们的基本模型中，一组决策者(S)通过控制一系列具有不确定结果的动作，动态地提炼了关于感兴趣的随机时变参数的信念。然后，这些参数被用来有效和稳健地控制物理系统。在这里，网络系统收集、处理和获取有关感兴趣的底层物理系统的信息，该系统用于控制它。这项拟议的工作将为随机变化的网络物理系统中的随机学习、决策和控制开发一个新的理论框架。为了获得对有效设计结构的分析见解，我们首先考虑网络系统的行为只影响底层物理系统的估计的情况。这类问题出现在与网络系统对物理系统状态的控制隔离的情况下对物理系统的(分布式)感知/跟踪。然后将考虑网络物理系统的联合状态估计和控制。在这里，最自然的第一步是获得充分的条件和/或特殊的系统类，其中信息获取和有效控制的分离方法是(接近)最优的。为了证明其在实践中的有效性，我们的理论框架将应用于数据中心的节能控制和有限感知下的智能电网的鲁棒控制的特定背景下。这项工作的智力价值将为包括信息获取、状态估计和控制在内的网络物理系统的设计开发一个理论框架。此外，还将探索分离状态估计和控制的最优性的分离定理。从更广泛的影响来看，通信网络闭合控制系统性能的显著提高将影响到广泛的社会效益应用，包括智能建筑、智能交通系统、节能数据中心和未来的智能电网。私人投资机构计划通过会议和期刊，以及组织与网络物理系统有关的专门讲习班和会议，广泛传播研究成果。拟议的项目将培训博士生，并丰富私人投资机构教授的通信、随机控制和网络课程。私人投资机构在多样性和外联活动方面有着良好的记录，这一项目将包括高中和本科生的接触和参与，包括代表性不足的少数群体和妇女。