Collaborative Research: An Economic Iterative Learning Control Framework with Application to Airborne Wind Energy Harvesting

合作研究：应用于机载风能采集的经济迭代学习控制框架

• 批准号: 1913735
• 负责人: Christopher Vermillion
• 金额: $ 16.75万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-16 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913735&HistoricalAwards=false
• 关键词: Collaborative; Research; Economic; Iterative; Learning

The objective of this project is to pioneer new techniques for controlling systems that operate in a repetitive manner, using information from previous iterations to improve the performance at each successive iteration. Unlike existing techniques that seek only to improve position tracking along a path, this research project will focus on the iteration-to-iteration improvement of economic metrics such as energy generation/expenditure, total iteration time, or monetary cost. The control techniques created in this research will be applicable to a wide variety of systems for which repetitive control is essential and there exists a clear economic objective to improve upon from one iteration to the next. Example applications include assembly line and manufacturing operations (an over $1 trillion industry where parts are produced by the thousands and minimizing manufacturing time and energy expenditure is critical), actively-controlled exoskeletons (which control a repetitive human walking gait), and airborne wind energy systems. This research will focus specifically on airborne wind energy systems, which replace the conventional tower with tethers and a lifting body to harness high altitude winds using very little material. These systems can generate substantially increased energy through repetitive crosswind flight, rather than stationary operation, and there exists a significant opportunity to improve the energy generation performance from one repetition to the next. The research will be augmented by education and outreach activities, including the creation of wind energy-inspired undergraduate classroom modules, development of a kite design activity at the Charlotte Engineering Early College High School, and summer opportunities at a regional airborne wind energy company, Windlift, Inc.This project will derive new control theoretic knowledge for a unique economic iterative learning control framework that focuses on maximizing or minimizing a profitability index rather than mere tracking performance. Specifically, the learning framework will blend two mechanisms that will transform the state of the art in point-to-point iterative learning control. First, unlike traditional point-to-point iterative learning control approaches where the waypoints are pre-specified and only the behavior between waypoints can be adapted from one iteration to the next, the framework will enable adaptation of the waypoints themselves from one iteration to the next. Secondly, an inner loop flexible-time iterative learning control module will allow the waypoint arrival times and total iteration time to vary from one iteration to the next. This will enable the research team to tackle time-optimal and energy-optimal problems through an iterative learning framework, which has not been accomplished in a general sense to-date. Considering that the waypoint adaptation law and flexible time iterative learning module comprise two interconnected subsystems, a small gain stability analysis framework will be used to derive bounds on the allowable variation of waypoints from one iteration to the next. Finally, the application of the ILC framework to repetitive crosswind flight of AWE systems will provide an online learning mechanism for the optimization of crosswind flight, which is especially important due to the complex and uncertain dynamic models of these systems (which can often render offline crosswind trajectory optimizations ineffective).

该项目的目的是使用以前的迭代中的信息来控制以重复性方式控制系统的新技术，以提高每个连续迭代的性能。与现有的技术仅寻求改善沿着路径的位置跟踪的技术不同，该研究项目将重点介绍经济指标的迭代对材料的改进，例如能源产生/支出，总迭代时间或货币成本。这项研究中创建的控制技术将适用于重复控制至关重要的多种系统，并且存在一个明确的经济目标，可以改善从一种迭代到下一种。示例应用程序包括装配线和制造业务（超过1万亿美元的行业，由成千上万的零件生产，最小化制造时间和能源消耗至关重要），积极控制的外骨骼（控制重复的人类步行步态）和机载风能系统。这项研究将专门集中在空气中的风能系统上，该系统用tethers代替了传统的塔楼，并使用很少的材料来替代抬起机身，以利用高海拔风。这些系统可以通过重复的交叉飞行而不是固定操作产生大幅增加的能量，并且有一个很大的机会来改善能源发电绩效，从一个重复到下一次重复。教育和外展活动将增加这项研究，包括创建以风能启发的本科教室模块，开发在夏洛特工程学院早期高中的风筝设计活动以及在区域空中风能公司的夏季机会，夏季的机会，风速，风能，Inc. Inc.这些项目将获得新的控制框架，以使其对唯一的经济型互动的介绍或分裂，该框架对独特的经济性互动，这是一个独特的控制理论，该框架的介绍是对独特的经济型号，该项目对唯一的经济性介绍，该项目对唯一的控制型介绍了，该项目是针对独特的互动，而不是分裂的。跟踪性能。具体而言，学习框架将融合两种机制，这些机制将在点对点迭代的学习控制中转变为最新技术。首先，与传统的点对点迭代学习控制方法不同，在该方法中，该方法是预先指定的，并且只能将航路点之间的行为从一个迭代中调整为下一个，框架将能够适应从一个迭代到下一个迭代的路点。其次，内部循环灵活的时间迭代学习控制模块将允许航路点到达时间和总迭代时间从一次迭代到下一个迭代。这将使研究团队能够通过迭代学习框架来解决时间最佳和能源最佳问题，这在迄今为止的一般意义上尚未完成。考虑到Waypoint适应定律和灵活的时间迭代学习模块包含两个相互联系的子系统，将使用一个较小的增益稳定性分析框架来得出从一个迭代到下一个迭代的允许变化的范围。最后，将ILC框架应用于AWE系统的重复性越野飞行将提供在线学习机制，以优化越界飞行，这尤其重要，因为这些系统的复杂和不确定的动态模型（通常可以使离线交叉风向轨迹轨迹优化效果不佳）。

项目成果

Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems

• DOI: 10.1109/tcst.2022.3165734
• 发表时间: 2022-11
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Mitchell Cobb;James Reed;Maxwell J. Wu;K. Mishra;K. Barton;C. Vermillion

Library-Based Norm-Optimal Iterative Learning Control

基于库的范数最优迭代学习控制

• DOI: 10.1109/cdc45484.2021.9682812
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Reed, James;Wu, Maxwell;Barton, Kira;Vermillion, Chris;Mishra, Kirti D.
• 通讯作者: Mishra, Kirti D.

An Iterative Learning Approach for Online Flight Path Optimization for Tethered Energy Systems Undergoing Cyclic Spooling Motion

循环绕线运动系留能源系统在线飞行路径优化的迭代学习方法

• DOI: 10.23919/acc.2019.8814773
• 发表时间: 2019
• 期刊: American Control Conference
• 作者: Cobb, Mitchell;Barton, Kira;Fathy, Hosam;Vermillion, Chris
• 通讯作者: Vermillion, Chris

Hierarchical Structures for Economic Repetitive Control

经济重复控制的层级结构

• DOI: 10.1109/cdc45484.2021.9683000
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Mishra, Kirti D.;Reed, James;Wu, Maxwell;Barton, Kira;Vermillion, Chris
• 通讯作者: Vermillion, Chris

Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems

• DOI: 10.1109/tcst.2019.2912345
• 发表时间: 2020-07-01
• 期刊: IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
• 影响因子: 4.8
• 作者: Cobb, Mitchell K.;Barton, Kira;Vermillion, Chris
• 通讯作者: Vermillion, Chris