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.

提案编号：ECCS-0801330提案标题：连续时间系统和图网络代理的自适应动态规划 PI 名称：Lewis, Frank L.PI 机构：德克萨斯大学阿灵顿分校目的和方法本研究的目的是提高人类工程的性能通过使用计算智能领域的技术设计更好的高性能自动反馈控制结构来改进系统。该方法是将自适应动态规划（ADP）方法用于连续时间工作的系统，这是一种尚未完全开发或用于控制系统设计的技术。 PI 之前的工作表明，连续时间系统的 ADP 为高性能控制系统设计提供了一种新颖的框架，从中出现了控制功能的多时间尺度分离，反映了人脑中自然的多时间尺度结构。智力优点。生物结构中的调节系统既是最优的又是实时自适应的，使在线学习能够达到最佳性能。然而，人造控制系统要么是最佳离线设计，要么是实时自适应方法。本研究将利用ADP开发实时最优、自适应的统一自动控制设计方法。将提供严格的数学性能证明。 更广泛的影响。这项工作将开发更好的反馈控制结构，从而促进飞机、发动机、工业过程等获得更好的性能。它将为将计算智能和控制系统社区聚集在一起的理论基础做出贡献。基于ADP的技术与标准自适应鲁棒控制技术之间的全面比较将通过机电实验室设备和三区域电力网络测试台上的实现进行。开发用于通信图系统的协作ADP算法将允许更有效的控制电力系统网络和车辆编队的研究。正在进行的研究课程开发活动将有助于将研究成果转化为 UTA 的教育和培训。 这笔赠款将允许扩展现有的自动化和机器人研究所 (ARRI) 项目，支持工程领域的女性、美国本科生研究和 K-12 推广。 屡获殊荣的 SBIR 计划将促进向美国工业界的技术转让。