  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.
