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.

该项目试图找到一个共同的决策框架，该框架无缝地集成了离线数据和计算，实时数据以及计算，学习和概率预测性决策。它为不确定的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。积分加强学习是整合实时数据驱动方法，基于模型的方法和物理约束的关键。将探索整体增强学习的结构，以确切地研究具有多个嵌套学习环的体系结构中的深度学习神经网络。然后将开发一个概率时空场景数据驱动的框架，以用于对不确定性下的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雕刻DC分销网络中电力电子转换器的最佳功率配置文件，并有助于减轻间歇源，不确定的负载需求或故障的不良影响。该项目代表了与退出的大数据和决策研究的根本性，而不是在不确定性构建中为增长量表和时间关键任务要求的系统制定自主决策。开发的算法和工具可以扩展到其他智能和连接的域，例如空中交通管理，网络交通排和传感器网络。预计到2020年，美国微电网容量将达到4.3 gw。直流分配网络是AC分配的替代品，对于可再生能源资源和电气运输机队的可扩展整合至关重要。研究结果将移植到加强学习，最佳控制，网络控制系统，数据驱动分析和决策以及电力电子系统方面的主题。该项目旨在使德克萨斯大学阿灵顿分校（UTA）与德克萨斯州A＆M-Corpus Christi（TAMUCC）之间的研究活动（HBCU/MI西班牙裔服务机构）均涉及电气工程和计算机科学背景的学生，这涉及NSF的法定任务和审查范围的范围。