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

该项目的目标是开拓新技术，用于控制以重复方式运行的系统，使用以前迭代的信息来提高每次连续迭代的性能。与现有的技术，只寻求改善位置跟踪沿着的路径，这个研究项目将集中在迭代到迭代的经济指标，如能源的产生/支出，总迭代时间，或货币成本的改善。在这项研究中创建的控制技术将适用于各种各样的系统，重复控制是必不可少的，并存在一个明确的经济目标，以改善从一个迭代到下一个。示例应用包括装配线和制造操作（超过1万亿美元的行业，其中零件由数千个生产，并且最小化制造时间和能量消耗是至关重要的），主动控制的外骨骼（其控制重复的人类行走步态）和空中风能系统。这项研究将特别关注空中风能系统，该系统用系绳和升力体取代传统的塔，以利用很少的材料来利用高空风。这些系统可以通过重复的逆风飞行而不是静止操作产生显著增加的能量，并且存在从一个重复到下一个重复提高能量产生性能的显著机会。这项研究将通过教育和推广活动得到加强，包括创建风能启发的本科课堂模块，在夏洛特工程学院早期高中开展风筝设计活动，以及在区域空中风能公司Windlift的夏季机会，该项目将为独特的经济迭代学习控制框架获得新的控制理论知识，该框架侧重于最大化或最小化盈利性指数，而不仅仅是跟踪性能。具体来说，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最新技术。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，路点是预先指定的，并且只有路点之间的行为可以从一个迭代适应到下一个迭代，该框架将使路点本身能够从一个迭代适应到下一个迭代。其次，内环灵活时间迭代学习控制模块将允许路径点到达时间和总迭代时间从一次迭代到下一次迭代而变化。这将使研究团队能够通过迭代学习框架来解决时间最优和能量最优的问题，这在一般意义上尚未实现。考虑到航路点自适应律和灵活时间迭代学习模块包括两个相互关联的子系统，将使用小增益稳定性分析框架来推导航路点从一次迭代到下一次迭代的允许变化的界限。最后，ILC框架在AWE系统的重复逆风飞行中的应用将为逆风飞行的优化提供在线学习机制，这对于这些系统的复杂和不确定的动态模型（通常会使离线逆风轨迹优化无效）尤为重要。

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).