Adaptive Dynamic Programming for Continuous-Time Systems and Networked Agents on Graphs

连续时间系统和图上网络代理的自适应动态规划

• 批准号: 0801330
• 负责人: Frank Lewis
• 金额: $ 25万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0801330&HistoricalAwards=false
• 关键词: Adaptive; Dynamic; Programming; Continuous; Time

Proposal Number: ECCS-0801330Proposal Title: Adaptive Dynamic Programming for Continuous-Time Systems and Networked Agents on GraphsPI Name: Lewis, Frank L.PI Institution: University of Texas at ArlingtonObjective and ApproachThe objective of this research is to improve the performance of human engineered systems by designing better high performance automatic feedback control structures using techniques from the Computational Intelligence field. The approach is to use adaptive dynamic programming (ADP) methods for systems working in continuous-time, a technique not yet fully developed or exploited for control systems design. The PI?s prior work shows that ADP for continuous-time systems provides a novel framework for high-performance control system design from which emerges a multi-timescale separation of control functionality mirroring natural multi-timescale structures in the human brain.Intellectual Merit.Regulation systems in biological structures are both optimal and adaptive in real-time, allowing on-line learning to achieve the best performance. However, manmade control systems are either optimal off-line designs or real-time adaptive methods. This research will use ADP to develop unified automatic control design methods that are both optimal and adaptive in real-time. Mathematically rigorous proofs of performance will be provided. Broader Impacts.This work will develop better feedback control structures that will facilitate better performance for aircraft, engines, industrial processes, etc. It will contribute to theoretical foundations bringing together the Computational Intelligence and Control Systems communities. Comprehensive comparisons between ADP-based techniques and standard adaptive & robust control techniques will be made through implementations on electro-mechanical laboratory equipment and on a three-area power network testbed.Development of cooperative ADP algorithms for systems on communication graphs will allow more effective control of networks of electric power systems and vehicle formations.Ongoing activities in research curriculum development will facilitate transferring research results to education and training at UTA. This grant will allow extension of existing Automation and Robotics Research Institute (ARRI) programs supporting women in engineering, US undergraduate student research, and K-12 outreach. The award-winning SBIR program will facilitate technology transfer to US industry.

建议编号：ECCS-0801330港口名称：用于连续时间系统和GraphSpi上的网络代理的自适应动态编程名称：Lewis，Lewis，Frank L.PI机构：德克萨斯州的ArlingtonObjective University at ArlingtonObjective和使用此研究的目的是通过使用更好的高性能反馈控制结构来提高人类工程系统的性能，从而提高人类工程的性能，从而提高自动反馈控制的结构。该方法是将自适应动态编程（ADP）方法用于连续工作的系统，这是一种尚未完全开发或利用用于控制系统设计的技术。 PI的先前工作表明，连续时间系统的ADP为高性能控制系统设计提供了一个新颖的框架，从中出现了控制功能的多频率分离，反映了人类大脑中自然的多时间计算结构。生物学结构中的调节系统在实时的实时和适应性上可以实现上线学习。但是，人造控制系统是最佳的离线设计或实时自适应方法。这项研究将使用ADP开发统一的自动控制设计方法，这些设计既是实时最佳又是适应性的。将提供数学上严格的性能证明。 这项工作将开发出更好的反馈控制结构，这将促进飞机，发动机，工业流程等更好的性能。它将有助于理论基础，将计算智能和控制系统社区融合在一起。 Comprehensive comparisons between ADP-based techniques and standard adaptive & robust control techniques will be made through implementations on electro-mechanical laboratory equipment and on a three-area power network testbed.Development of cooperative ADP algorithms for systems on communication graphs will allow more effective control of networks of electric power systems and vehicle formations.Ongoing activities in research curriculum development will facilitate transferring research results to education and training at UTA. 这项赠款将允许扩展现有的自动化和机器人研究研究所（ARRI）计划，以支持工程领域的妇女，美国本科生研究和K-12外展。 屡获殊荣的SBIR计划将促进技术转移到美国行业。