Learning Time Improvements to an Evolutionary Algorithm for Online, Non-Stationary, Optimization of Flight Control in a Flapping Wing Micro Air Vehicle

Small Flapping-Wing Micro-Air Vehicles (FW-MAVs) can experience wing damage and wear while in service. Even small amounts of such damage require significant adaptation of on-board flight control to maintain flight path precision. Previous work employed a custom Evolutionary Algorithm (EA) that adapted wing motion patterns, while in flight and in normal online service, to compensate for wing damage. Although successful in finding solutions to this challenging online non-stationary problem, the previous methods would often require hours of flight time to reach full success. This paper details a completely new EA and wing motion basis function set that cuts the learning time to on average four minutes while not sacrificing final controller quality. The paper will provide both conceptual discussion of how the new algorithm delivers such dramatic improvements and extensive simulation results demonstrating robustness and consistently low time requirements over a wide variety of wing damage conditions.