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: 1408022
• 负责人: Silvia Ferrari
• 金额: $ 23.3万
• 依托单位: Duke 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 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408022&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，用于复杂系统的管理和控制，其中智能体以完全合作的方式共同工作，以最大限度地提高全局性能。作为证明新方法价值的试验平台，它们将模拟以下挑战：（1）地震反应，这是一类发生非常迅速、筹备时间短、地理范围有限的灾害;（2）干旱引起的饥荒救济，这是一类预测和筹备时间较长、地理范围较大的灾害。研究结果将被广泛传播，并将被纳入教育和推广计划，包括杜克大学的本科生研究经验（REU）网站和美国国家科学基金会资助的IGERT无线智能传感器网络，为暑期学校和国际合作伙伴提供信息。这里的关键挑战是开发一个新版本的自适应近似动态规划（ADP），它是完全分布式的，解决多尺度动力系统的问题。这项工作建立在最近的工作的铅PI分布式最优控制（DOC），并包括最佳限制运营商的降维系统的一部分，并利用偏微分方程（PDE）和随机微分方程（PDE）领域的方法的发展。