Flapping wing micro air vehicles (FWMAV's) are a subcategory of unmanned aerial vehicle which use flapping wings for thrust generation. The high agility and maneuverability of FWMAV's are very favorable attributes, making them more applicable in cluttered spaces. A tailless FWMAV called the Delfly Nimble has been developed at the Delft University of Technology. Due to the inherent instability of the tailless design an active controller is required to ensure safe and stable flight of the drone. In previous research, models have been developed for the longitudinal dynamics of the Delfly Nimble. In this paper, a grey-box state-space model of the lateral body dynamics in hover conditions is identified using system identification techniques. The parameters which needed to be estimated were stability and control derivatives, and they were obtained with a least-squares approach. Free-flight experiments were performed to generate the identification and validation data. A doublet train was used in the identification experiments, with the gains of the controller adjusted in such a way that maximum excitation was acquired. The identified model has been validated with various maneuvers. These included doublets, 112-maneuvers, maneuvers using coupled inputs, and maneuvers with sideways flight. The resulting model is able to predict the state derivatives of most maneuver accurately, reaching accuracies of over 90% for maneuvers close to hover. Moreover, in closed-loop configuration it is able to simulate the state response accurately, with accuracies of over 85% for maneuvers close to hover, and remains stable, making it applicable for controller design and stability analysis. Finally, based on the model the inherent instability of the lateral body dynamics was also confirmed, for there are eigenvalues with positive real parts.

Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel Classification Algorithm for Machine Learning control (CAML) is presented, which uses model identification and a neural network classifier to select from several predefined gain sets. The algorithms show comparable performance when considering variability only, but the Policy Gradient algorithm is more robust to noise, disturbances, nonlinearities and flapping motion.