Collaborative Research: Multi-Scale, Multi-Rate Spatiotemporal Optimal Control with Application to Airborne Wind Energy Systems

合作研究：多尺度、多速率时空最优控制及其在机载风能系统中的应用

• 批准号: 1711579
• 负责人: Christopher Vermillion
• 金额: $ 23.06万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711579&HistoricalAwards=false
• 关键词: Collaborative; Research; Multi; Scale; Rate

The objective of this research is to pioneer new control strategies for emerging systems whose operating environments change as functions of both time and a controllable spatial position. Such applications include coordinated unmanned aerial vehicles that operate in variable atmospheric conditions, concepts for relocatable marine hydrokinetic energy systems that operate in a varying ocean environment, and airborne wind energy systems that operate in an atmospheric environment where the wind varies with respect to both time and vertical position. This research will focus on deriving general theory that will be relevant to a variety of applications, along with the validation of these results on an airborne wind energy system. In airborne wind energy systems, the conventional tower is replaced by tethers and a lifting body (a wing or aerostat) that elevates a horizontal-axis turbine to high altitudes. Because the tether lengths can be adjusted, the operating altitude can be varied to optimally harness the wind resource. The work will include a substantial complementary educational component, wherein graduate, undergraduate, and STEM high school students will utilize NREL's Hybrid Optimization Model for Multiple Energy Resources (HOMER) software to optimize renewable/storage/dispatchable network configurations for microgrids in North Carolina and California.This research will derive new control theoretic knowledge and tools for the systems that operate in a spatiotemporally varying and partially observable environment. While optimal control in a temporally varying environment is a well-studied problem that can be addressed through Markov models, the addition of a spatial component results in an explosion in the number of states, rendering Markov-based methods computationally infeasible in most cases. Furthermore, the presence of partial observability results in a fundamental tradeoff between exploration (obtaining knowledge of the spatial environment) and exploitation (operating at the most favorable locations). To address the complexities of this spatiotemporal optimization problem, this research will explore the use of a multi-rate, multi-scale hierarchical framework. Specifically, an upper-level controller will perform a global optimization over a very coarse grid (thereby rendering the optimization computationally tractable), and a lower-level optimization will perform adjustments on a much finer grid. The research will focus on model predictive control for the upper-level optimization and will explore the use of extremum seeking and model predictive control strategies at the lower level. Control algorithms will be validated on a model of a lighter-than-air airborne wind energy system, using real wind shear profile models and load demand data. In this airborne wind energy system, the wind speed is only measurable at the system?s operating altitude (thereby making the problem partially observable), and significant energy production improvements can be realized through the optimal adjustment of the operating altitude.uction improvements can be realized through the optimal adjustment of the operating altitude.

这项研究的目的是为新兴系统开创新的控制策略，这些系统的运行环境随着时间和可控空间位置的变化而变化。此类应用包括在可变大气条件下运行的协调无人机、在变化的海洋环境中运行的可重新定位海洋流体动力能源系统的概念，以及在风随时间和垂直位置变化的大气环境中运行的机载风能系统。这项研究将侧重于推导与各种应用相关的一般理论，以及在机载风能系统上验证这些结果。在机载风能系统中，传统的塔被系绳和提升体（机翼或浮空器）取代，将水平轴涡轮机提升到高空。由于系绳长度可以调节，因此可以改变操作高度，以最佳地利用风力资源。这项工作将包括一个实质性的补充教育部分，其中研究生、本科生和 STEM 高中生将利用 NREL 的多能源混合优化模型 (HOMER) 软件来优化北卡罗来纳州和加利福尼亚州微电网的可再生/存储/可调度网络配置。这项研究将为在时空变化和部分运行的系统导出新的控制理论知识和工具。 可观察的环境。虽然时间变化环境中的最优控制是一个经过充分研究的问题，可以通过马尔可夫模型来解决，但空间分量的添加会导致状态数量的爆炸，使得基于马尔可夫的方法在大多数情况下在计算上不可行。此外，部分可观测性的存在导致探索（获得空间环境的知识）和开发（在最有利的位置进行操作）之间的基本权衡。为了解决这种时空优化问题的复杂性，本研究将探索使用多速率、多尺度分层框架。具体来说，上层控制器将在非常粗糙的网格上执行全局优化（从而使优化在计算上易于处理），而下层优化将在更精细的网格上执行调整。该研究将重点关注上层优化的模型预测控制，并将探索极值搜索和模型预测控制策略在下层的使用。将使用真实的风切变剖面模型和负载需求数据，在轻于空气的机载风能系统模型上验证控制算法。在该机载风能系统中，风速只能在系统的工作高度测量（从而使问题部分可观察），通过工作高度的优化调整可以实现发电量的显着提高。通过工作高度的优化调整可以实现吸力的改善。