Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

IEEE Access

Hailong Pei¹  Zihuan Cheng¹  Shuai Li² 

[1] Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou, China;School of Engineering, Swansea University, Swansea, U.K;

关键词: Ducted fan;    fast convergence;    high-speed flight;    neural networks;    transition control;    unmanned aerial vehicle (UAV);   

DOI  :  10.1109/ACCESS.2020.2997877

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown