  Tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles
(UAVs) have the capability of hovering and performing efficient level flight
with compact mechanical structures. We present a unified controller design for
such UAVs, based on recurrent neural networks. An advantage of this design
method is that the various flight modes (i.e., hovering, transition and level
flight) of a VTOL UAV are controlled in a unified manner, as opposed to
treating them separately and in the runtime switching one from another. The
proposed controller consists of an outer-loop position controller and an
inner-loop attitude controller. The inner-loop controller is composed of a
proportional attitude controller and a loop-shaping linear angular rate
controller. For the outer-loop controller, we propose a nonlinear solver to
compute the desired attitude and thrust, based on the UAV dynamics and an
aerodynamic model, in addition to a cascaded PID controller for the position
and velocity tracking. We employ a recurrent neural network (RNN) to
approximate the behavior of the nonlinear solver, which suffers from high
computational complexity. The proposed RNN has negligible approximation errors,
and can be implemented in real-time (e.g., 50 Hz). Moreover, the RNN generates
much smoother outputs than the nonlinear solver. We provide an analysis of the
stability and robustness of the overall closed-loop system. Simulation and
experiments are also presented to demonstrate the effectiveness of the proposed
method.
