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.

该项目重点研究网络物理系统背景下的信息获取、状态估计和控制问题。在我们的基础模型中，（一组）决策者通过控制一系列具有不确定结果的行动，动态地完善关于随机时变感兴趣参数的信念。然后使用这些参数来有效、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，这些信息用于其控制。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仅影响底层物理系统的估计的情况。此类问题出现在与物理系统状态的网络系统控制隔离的物理系统的（分布式）感测/跟踪的背景下。然后将考虑网络物理系统的联合状态估计和控制。这里最自然的第一步是获得足够的条件和/或特殊类别的系统，其中信息获取和有效控制的分离方法是（接近）最佳的。为了证明其在实践中的实用性，我们的理论框架将应用于数据中心的节能控制和有限传感下智能电网的鲁棒控制的特定背景。这项工作的智力价值将是开发一个用于设计信息物理系统的理论框架，包括信息获取、状态估计和控制。此外，还将探索分离状态估计和控制的最优性的分离定理。就更广泛的影响而言，通过通信网络封闭的控制系统的显着性能改进将影响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智能电网。 PI 计划通过会议和期刊以及组织与网络物理系统相关的专门研讨会和会议来广泛传播研究成果。拟议的项目将培养博士。学生并丰富 PI 在通信、随机控制和网络方面教授的课程。 PI 在多样性和外展活动方面拥有良好的记录，在该项目中，这些活动将包括高中生和本科生的接触和参与，其中包括代表性不足的少数族裔和女性。