An Iterative Learning Approach for Online Flight Path Optimization for Tethered Energy Systems Undergoing Cyclic Spooling Motion

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.