Disturbance observer-based discrete-time neural control for unmanned aerial vehicles with uncertainties and disturbances

被引：1
|
作者：
Shao, Shuyi [1 ]
Chen, Mou [1 ]
Mei, Rong [2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Jiangsu, Peoples R China
[2] Nanjing Forest Police Coll, Criminal Invest Dept, Nanjing 210023, Jiangsu, Peoples R China
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
基金：
中国国家自然科学基金;
关键词：
UAV; disturbance observer; neural network; backstepping; tracking control; NONLINEAR-SYSTEMS; FLIGHT CONTROL; TRACKING; NETWORK; DESIGN;
D O I：
10.1016/j.ifacol.2017.08.2439
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a disturbance observer-based discrete-time neural control problem is studied for unmanned aerial vehicle (UAV) in the presence of external disturbances and system uncertainties. To estimate the external disturbance, a nonlinear discrete-time disturbance observer (DTDO) is designed. Furthermore, the system uncertainties are approximated by employing neural network (NN). Then, a discrete-time neural tracking control scheme is proposed based on the designed DTDO, the discrete-time tracking differentiator and the backstepping technique. Under the discrete-time Lyapunov analysis, the boundness of all the closed-loop system signals are proven. Finally, numerical simulation results are shown to demonstrate the effectiveness of the proposed control scheme. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：15289 / 15294
页数：6
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