Neural network and disturbance observer based control for automatic carrier landing of UAV

被引：0

作者：

Hu W. ^{[1
]}

Wan W. ^{[2
]}

Chen M. ^{[1
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] AVIC Shenyang Aircraft Design and Research Institute, Shenyang

来源：

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2022年 / 43卷

关键词：

automatic carrier landing control; disturbance observer; flight control; neural network; shipboard unmanned aerial vehicle;

D O I：

10.7527/S1000-6893.2022.26963

中图分类号：

学科分类号：

摘要：

An automatic carrier landing control method based on the neural network and disturbance observer is proposed for the Unmanned Aerial Vehicle（UAV）with dynamic disturbance. The neural network is employed to deal with system uncertainty，and the disturbance observer is employed to estimate the external disturbance of the UAV. By using the neural network output and disturbance observer output，a nonlinear automatic carrier landing controller for the UAV is designed to realize effective tracking of the landing control command. Utilizing the Lyapunov stability method，the convergence of all the closed-loop signals is proved strictly，ensuring the precise automatic carrier landing control of the UAV. Simulation results show the effectiveness of the proposed control method. © 2022 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.

引用

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