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This paper presents an iterative learning based approach for optimizing the crosswind flight path of an energy-harvesting tethered system that executes cyclic spool-in/spool-out motions. Through the combination of a high-tension crosswind spool-out motion (made possible through a high lift wing) and low-tension spool-in motion, net energy is generated at every cycle. Because the net energy generated by the system is highly sensitive to the crosswind flight patterns used on spool-out, and because the motions of the system are repetitive, we use an iterative learning formulation to optimize the flight patterns in real time. Using a medium-fidelity dynamic model, we demonstrate that an optimization approach based on iterative learning control (ILC) significantly increases the average power generated by such a system. 

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.