Iterative learning-based waypoint optimization for repetitive path planning, with application to airborne wind energy systems
Iterative learning-based waypoint optimization for repetitive path planning, with application to airborne wind energy systems
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基于迭代学习的航路点优化，用于重复路径规划，并应用于机载风能系统
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
C. Vermillion
中科院分区：
文献类型：
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作者：
Mitchell Cobb;K. Barton;H. Fathy;C. Vermillion
本文提出了一种迭代学习方法，用于优化重复路径跟踪应用中的航路点。我们提出的算法包括两个关键特征：第一，递归最小二乘拟合用于构造性能指标的行为的估计。其次，迭代到迭代的航路点自适应律被用来更新航路点的最佳性能的方向。该航路点更新法与传统的迭代学习控制（ILC）更新的数学结构并行，但用当前和估计的最优航路点序列之间的误差代替跟踪误差项。所提出的方法被应用到一个空中风能（AWE）系统的迎风路径优化，其目标是最大限度地提高平均功率输出超过一个图-8 path. In验证的工具，从这项工作中，我们引入了一个简化的二维模拟到更复杂的三维AWE系统，蒸馏的问题，其核心元素。使用这个模型，我们证明了所提出的路点自适应策略成功地实现了收敛到接近最佳的图8路径的各种初始条件。
This paper presents an iterative learning approach for optimizing waypoints in repetitive path following applications. Our proposed algorithm consists of two key features: First, a recursive least squares fit is used to construct an estimate of the behavior of the performance index. Secondly, an iteration-to-iteration waypoint adaptation law is used to update waypoints in the direction of optimal performance. This waypoint update law parallels the mathematical structure of a traditional iterative learning control (ILC) update but replaces the tracking error term with an error between the present and estimated optimal waypoint sequences. The proposed methodology is applied to the crosswind path optimization of an airborne wind energy (AWE) system, where the goal is to maximize the average power output over a figure-8 path. In validating the tools from this work, we introduce a simplified 2-dimensional analog to the more complex 3-dimensional AWE system, which distills the problem to its core elements. Using this model, we demonstrate that the proposed waypoint adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions.
影响因子：
4.8
作者：
C. Freeman;Zhonglun Cai;E. Rogers;P. Lewin
通讯作者：
C. Freeman;Zhonglun Cai;E. Rogers;P. Lewin