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This paper presents an iterative learning approach for optimizing 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 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.
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On Wind Speed Sensor Configurations and Altitude Control in Airborne Wind Energy Systems
Real-time altitude control of airborne wind energy (AWE) systems can improve performance by allowing turbines to track favorable wind speeds across a range of operating altitudes. The current work explores the performance implications of deploying an AWE system with sensor configurations that provide different amounts of data to characterize wind speed profiles. We examine various control objectives that balance trade-offs between exploration and exploitation, and use a persistence model to generate a probabilistic wind speed forecast to inform control decisions. We assess system performance by comparing power production against baselines such as omniscient control and stationary flight. We show that with few sensors, control strategies that reward exploration are favored. We also show that with comprehensive sensing, the implications of choosing a sub-optimal control strategy decrease. This work informs and motivates the need for future research exploring online learning algorithms to characterize vertical wind speed profiles.
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- Award ID(s):
- 1709767
- PAR ID:
- 10121159
- Date Published:
- Journal Name:
- 2019 American Control Conference (ACC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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