EAGER: Real-Time: Collaborative Research: Unified Theory of Model-based and Data-driven Real-time Optimization and Control for Uncertain Networked Systems

EAGER：实时：协作研究：不确定网络系统基于模型和数据驱动的实时优化与控制的统一理论

基本信息

批准号：
1953049
负责人：
Junfei Xie
金额：
$ 5.09万
依托单位：
San Diego State University Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953049&HistoricalAwards=false
关键词：
EAGER Real Time Collaborative Research

项目摘要

The project seeks to find a common decision-making framework that seamlessly integrates offline data and computing, real-time data and computing, learning, and probabilistic predictive decision. It provides a unified theory of model-based and data-driven real-time optimization and control for uncertain networked systems. Integral Reinforcement Learning holds the key to integrating real-time data-driven methods, model-based methods, and physical constraints. The structure of Integral Reinforcement Learning will be explored to investigate exactly how and where to use Deep Learning neural networks in architectures that have multiple nested learning loops. A probabilistic spatiotemporal scenario data-driven framework will then be developed for multi-scale sequential control of networked engineering systems under uncertainty. The algorithms and tools developed will be used to sculpt optimal power profiles for power electronics converters in a DC distribution network and help mitigate the adverse effects of intermittent sources, uncertain load demand, or faults. The project represents a radical departure from the exiting big data and decision-making research, toward developing autonomous decision-making under uncertainty constructs for systems of growing scales and time critical mission requirements. Algorithms and tools developed can be extended to other smart and connected domains, e.g., air traffic management, networked traffic platoons, and sensor networks. US microgrid capacity is expected to reach 4.3 GW by 2020. DC distribution networks are emerging alternatives to AC distribution ones, and are critical to the scalable integration of renewable energy resources and electrified transportation fleets. Research results will be ported into topics in reinforcement learning, optimal control, networked control systems, data-driven analysis and decision-making, and power electronics systems. This project synergizes research activities between University of Texas at Arlington (UTA) and Texas A&M-Corpus Christi (TAMUCC), both HBCU/MI Hispanic Serving Institutions, and involves students from Electrical Engineering and Computer Science backgrounds.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.

该项目寻求找到一个通用的决策框架，无缝集成离线数据和计算、实时数据和计算、学习和概率预测决策。它为不确定网络系统提供了基于模型和数据驱动的实时优化和控制的统一理论。整体强化学习掌握着集成实时数据驱动方法、基于模型的方法和物理约束的关键。将探索整体强化学习的结构，以准确研究在具有多个嵌套学习循环的架构中如何以及在何处使用深度学习神经网络。然后将开发一个概率时空场景数据驱动框架，用于不确定性下网络工程系统的多尺度顺序控制。开发的算法和工具将用于为直流配电网络中的电力电子转换器塑造最佳功率曲线，并帮助减轻间歇性电源、不确定的负载需求或故障的不利影响。该项目与现有的大数据和决策研究截然不同，致力于在不确定性结构下为规模不断扩大和时间关键任务要求的系统开发自主决策。开发的算法和工具可以扩展到其他智能和互联领域，例如空中交通管理、网络交通队列和传感器网络。到 2020 年，美国微电网容量预计将达到 4.3 吉瓦。直流配电网络是交流配电网络的新兴替代品，对于可再生能源资源和电气化运输车队的可扩展整合至关重要。研究成果将被移植到强化学习、最优控制、网络控制系统、数据驱动分析和决策以及电力电子系统等领域。该项目协同了德克萨斯大学阿灵顿分校 (UTA) 和德克萨斯农工大学科珀斯克里斯蒂分校 (TAMUCC) 之间的研究活动，这两个机构都是 HBCU/MI 西班牙裔服务机构，并涉及来自电气工程和计算机科学背景的学生。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。