Full Attitude Control of an Efficient Quadrotor Tail-sitter VTOL UAV with Flexible Modes

In this paper, we present a full attitude control of an efficient quadrotor tail-sitter VTOL UAV with flexible modes. This control system is working in all flight modes without any control surfaces but motor differential thrusts. This paper concentrates on the design of the attitude controller and the altitude controller. For the attitude control, the controller's parameters and filters are optimized based on the frequency response model which is identified from the sweep experiment. As a result, the effect of system flexible modes is easily compensated in frequency-domain by using a notch filter, and the resulting attitude loop shows superior tracking performance and robustness. In the coordinated flight condition, the altitude controller is structured as the feedforward-feedback parallel controller. The feedforward thrust command is calculated based on the current speed and the pitch angle. Tests in hovering, forward accelerating and forward decelerating flights have been conducted to verify the proposed control system

Control of a Tail-Sitter VTOL UAV Based on Recurrent Neural Networks

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