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CPS: Synergy: Collaborative Research: Hybrid Control Tools for Power Management and Optimization in Cyber-Physical Systems

CPS：协同：协作研究：用于网络物理系统中的电源管理和优化的混合控制工具

基本信息

批准号：
1239225
负责人：
Magnus Egerstedt
金额：
$ 80万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-10-01 至 2016-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1239225&HistoricalAwards=false
关键词：
CPS Synergy Collaborative Research Hybrid

项目摘要

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors.The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

该项目探索通过在不同操作模式（例如，低功率/高功率、开/关或移动的/静态）。主要的理论贡献是一个计算，混合最优控制框架，连接到一些相关的目标应用程序的物理建模，控制设计和软件架构都构成重要组成部分。该计划的基础研究沿着沿着四个不同的维度推进最先进的技术水平，即（1）用于功率管理的实时混合最优控制算法，（2）移动的传感器网络中的功率管理，（3）用于基础设施管理的分布式功率感知架构，以及（4）嵌入式多核处理器中的功率管理。其使得能够以更有效的方式部署低功率设备，对许多应用领域具有影响，包括分布式传感器和通信网络以及智能和高效的建筑物。该团队代表了一所研究型大学（格鲁吉亚理工学院）和一所本科教学型大学（宾夕法尼亚州约克学院），以确保教育部分具有深远的影响，并跨越传统的教育界限。该项目涉及新颖的，基于归纳的学习模块，研究生与本科研究人员合作。