Floating offshore wind turbines (FOWT) are exposed to hard environmental conditions which could impose expensive maintenance operations. These costs could be alleviated by monitoring these floating devices using UAVs. Given the FOWT location, UAVs are currently the only way to do this health monitoring. But this means that UAV should be well equipped and must be accurately controlled. Rotational inertia variation is a common disturbance that affect the aerial vehicles during these inspection tasks. To address this issue, in this work we propose a new neural controller based on adaptive neuro estimators. The approach is based on the hybridization of feedback linearization, PIDs and artificial neural networks. Online learning is used to help the network to improve the estimations while the system is working. The proposal is tested by simulation with several complex trajectories when the rotational inertia is multiplied by 10. Results show the proposed UAV neural controller gets a good tracking and the neuro estimators tackle the effect of the variations of the rotational inertia.