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

这个项目的目标是开创新的技术，用于控制以重复方式运行的系统，使用来自以前迭代的信息来提高每次连续迭代的性能。与现有的只寻求改进路径上的位置跟踪的技术不同，该研究项目将专注于经济指标的迭代改进，如能源产生/支出、总迭代时间或货币成本。在这项研究中创建的控制技术将适用于各种各样的系统，对于这些系统，重复控制是必不可少的，并且存在一个明确的经济目标，可以从一次迭代到下一次迭代进行改进。示例应用包括装配线和制造操作(一个价值超过1万亿美元的行业，零部件由数千人生产，最大限度地减少制造时间和能源消耗至关重要)、主动控制的外骨骼(控制人类重复行走的步态)，以及机载风能系统。这项研究将特别关注机载风能系统，它用绳索和升降体取代传统的塔架，以使用非常少的材料来利用高空风。这些系统可以通过重复的侧风飞行而不是固定的操作来产生显著增加的能量，并且存在着从一次重复到下一次提高发电性能的重大机会。这项研究将通过教育和推广活动得到加强，包括创建以风能为灵感的本科课堂模块，在夏洛特工程大学早期高中开发风筝设计活动，以及在地区机载风能公司WindLift，Inc.提供暑期机会。该项目将为一个独特的经济迭代学习控制框架获得新的控制理论知识，该框架专注于最大化或最小化盈利指数，而不仅仅是跟踪性能。具体地说，学习框架将混合两种机制，这两种机制将改变点对点迭代学习控制的技术水平。首先，与传统的点对点迭代学习控制方法不同，在传统的点对点迭代学习控制方法中，航路点是预先指定的，只有航路点之间的行为可以从一个迭代调整到下一个迭代，该框架将允许从一个迭代到下一个迭代调整航路点本身。其次，内环弹性时间迭代学习控制模块将允许航点到达时间和总迭代时间在一次迭代到下一次迭代之间变化。这将使研究小组能够通过迭代学习框架处理时间最优和能量最优的问题，这是迄今为止尚未在一般意义上实现的。考虑到路点自适应律和灵活时间迭代学习模块由两个相互连接的子系统组成，将使用一个小增益稳定性分析框架来推导出每一次迭代的路点允许变化界。最后，将ILC框架应用于AWE系统的重复侧风飞行，将为侧风飞行的优化提供一种在线学习机制，这一点尤其重要，因为这些系统的动态模型复杂且不确定(这往往会使离线侧风轨迹优化无效)。

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).