A reinforcement learning approach to lift generation in flapping MAVs: simulation results

Flapping micro aerial vehicles are interesting in applications where maneuverability is needed in confined spaces. Yet aerodynamics of insect flapping flight is not completely known and the research in this area lacks a suitable aerodynamic model that can be used for control purposes. Reinforcement learning approach is proposed which is inspired from "how" the learning is achieved in real insects in nature. The reinforcement learning controller has been simulated using a quasi-steady aerodynamic model and shown to converge to a flapping motion. The learning capacity and advantages of this approach are also discussed