Collaborative Research: A Distributed Approximate Dynamic Programming Approach for Robust Adaptive Control of Multiscale Dynamical Systems

协作研究：多尺度动力系统鲁棒自适应控制的分布式近似动态规划方法

• 批准号: 1406224
• 负责人: Yannis Kevrekidis
• 金额: $ 16.7万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406224&HistoricalAwards=false
• 关键词: Collaborative; Research; Distributed; Approximate; Dynamic

This project will develop a new type of general multiagent system for management and control of complex systems, in which the agents work together in a fully cooperative way to maximize global performance over time, in the face of nonlinearity and complexity. As a testbed to prove the value of the new approach, they will simulate the challenges of: (1) earthquake response, representing a class of disasters with very rapid occurrence, short lead times and restricted geographic extent; and (2) drought-induced famine relief, representing the class of disasters with longer forecast and lead times, and larger geographic extent. The results will be widely disseminated and will feed into programs for education and outreach, including a Research Experience for Undergraduates (REU) site at Duke and the NSF-funded IGERT on Wireless Intelligent Sensor Networks, feeding into summer schools and international partnerships.The key challenge here is to develop a new version of adaptive, approximate dynamic programming (ADP) which is fully distributed,to address the case of multiscale dynamical systems. The work builds on recent work of the lead PI on Distributed Optimal Control (DOC),and includes development of optimal restriction operators for dimensionality reduction in parts of the system, and exploitation of methods from the field of partial differential equations (PDE) and stochastic differential equations (SDE).

该项目将开发一种新型的通用多智能体系统，用于管理和控制复杂系统，其中智能体以完全合作的方式一起工作，以随着时间的推移，面对非线性和复杂性，最大化全局性能。作为证明新方法价值的试验台，他们将模拟以下挑战：（1）地震响应，代表一类发生速度非常快、交付周期短且地理范围有限的灾害； (2) 干旱引起的饥荒救济，代表预报和准备时间较长、地理范围较大的灾害类别。研究结果将广泛传播，并将纳入教育和推广项目，包括杜克大学本科生研究体验 (REU) 网站和 NSF 资助的无线智能传感器网络 IGERT，并纳入暑期学校和国际合作伙伴关系。这里的挑战是开发一种新版本的完全分布式的自适应近似动态规划（ADP），以解决多尺度动力系统的情况。这项工作建立在主要 PI 最近关于分布式最优控制 (DOC) 的工作的基础上，包括开发用于系统部分降维的最优限制算子，以及开发偏微分方程 (PDE) 和随机方程领域的方法微分方程（SDE）。