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In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course.

In this paper, we address the hover-to-cruise transition control problem for high-speed level flight of a ducted fan UAV. Firstly, the nonlinear aircraft dynamic system is derived and relevant key aerodynamic characteristics are presented. Then, we divide the dynamic system into known parts and uncertain parts. In order to track a reference system formed by the known parts, we propose a neural-network-based control strategy to learn the error dynamics and cancel the tracking error with proved stability. Finally, simulations and experiments are conducted to perform the transition process at fixed altitude and constant horizontal acceleration, with satisfactory results accomplishing the designed flight course.