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This paper focuses on the empirical derivation of regret bounds for mobile systems that can vary their locations within a spatiotemporally varying environment in order to maximize performance. In particular, the paper focuses on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including a maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches. 

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.