摘要
针对干扰下的舰载无人机(UAV)自动着舰问题,提出了一种融合神经网络与干扰观测器的自动着舰控制方法。首先采用神经网络处理系统不确定引入干扰观测器逼近系统外部干扰,同时基于神经网络输出和干扰观测器输出设计了非线性自动着舰控制器,实现了对着舰指令的有效跟踪。然后利用李雅普诺夫稳定性方法严格证明了所有闭环系统信号的收敛性,保证了舰载UAV自动精确着舰控制。最后进行数字仿真验证,仿真结果表明设计的基于神经网络和干扰观测器的自动着舰控制方法是有效的。
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.
作者
胡伟
万文章
陈谋
HU Wei;WAN Wenzhang;CHEN Mou(College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110035,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2022年第S01期119-128,共10页
Acta Aeronautica et Astronautica Sinica
基金
江苏省重点研发计划(社会发展)(BE2020704)
航空科学基金(20200007052001)
关键词
舰载无人机
飞行控制
自动着舰控制
神经网络
干扰观测器
shipboard unmanned aerial vehicle
flight control
automatic carrier landing control
neural network
disturbance observer
作者简介
通讯作者:陈谋,E-mail:chenmou@nuaa.edu.cn。
