Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems
Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems
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DOI：
10.1109/tcst.2019.2912345
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发表时间：
2020-07-01
影响因子：
4.8
通讯作者：
Vermillion, Chris
中科院分区：
文献类型：
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作者：
Cobb, Mitchell K.;Barton, Kira;Vermillion, Chris
本文提出了一种迭代学习方法来优化重复路径跟踪应用中的课程几何。特别是，我们关注机载风能(AWE)系统。我们提出的算法由两个关键特征组成。首先，使用递归最小二乘(RLS)拟合来构造性能指标行为的估计。其次，采用迭代-迭代路径自适应律在性能最优的方向调整路径形状。我们提出了两个候选更新律，它们都与普通迭代学习控制(ILC)更新律的数学结构相似，但都用基于性能指标的项取代了与跟踪相关的项。我们将我们的公式应用于AWE系统的迭代侧风路径优化，其中的目标是最大化图8路径上的平均功率输出。使用基于物理的AWE系统模型，我们证明了所提出的自适应策略在恒定和真实风廓线下在各种初始条件下成功地收敛到接近最优的8字形路径。
This paper presents an iterative learning approach for optimizing the course geometry in repetitive path following applications. In particular, we focus on airborne wind energy (AWE) systems. Our proposed algorithm consists of two key features. First, a recursive least squares (RLS) fit is used to construct an estimate of the behavior of the performance index. Second, an iteration-to-iteration path adaptation law is used to adjust the path shape in the direction of optimal performance. We propose two candidate update laws, both of which parallel the mathematical structure of common iterative learning control (ILC) update laws but replace the tracking-dependent terms with terms based on the performance index. We apply our formulation to the iterative crosswind path optimization of an AWE system, where the goal is to maximize the average power output over a figure-8 path. Using a physics-based AWE system model, we demonstrate that the proposed adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions under both constant and real wind profiles.