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基于自适应动态逆的自动空中加油轨迹跟踪
引用本文:盖文东,王宏伦,李大伟.基于自适应动态逆的自动空中加油轨迹跟踪[J].北京航空航天大学学报,2012,38(5):585-590.
作者姓名:盖文东  王宏伦  李大伟
作者单位:北京航空航天大学飞行器控制一体化技术重点实验室,北京,100191;北京航空航天大学飞行器控制一体化技术重点实验室,北京,100191;北京航空航天大学飞行器控制一体化技术重点实验室,北京,100191
基金项目:中央高校基本科研业务费专项资金资助项目
摘    要:针对自动空中加油(AAR, Automated Aerial Refueling)的受油机轨迹跟踪控制问题,采用导引环与姿态控制环分离的控制策略,提出了基于非线性路径跟随导引和自适应神经网络动态逆的受油机轨迹跟踪控制方法.该方法将改进的非线性导引应用于受油机横、纵向导引控制,使用自适应神经网络在姿态角速率回路补偿外界干扰和系统模型误差,并利用比例-积分型姿态角速率误差动态方程设计自适应神经网络权值更新律.仿真结果表明:在受油机接近加油机过程中,该方法有效提高了受油机轨迹跟踪控制系统的抗干扰能力和对模型不确定性的自适应能力,能够满足自动空中加油的控制要求.

关 键 词:自动空中加油  非线性导引  轨迹跟踪  自适应神经网络
收稿时间:2011-03-01

Trajectory tracking for automated aerial refueling based on adaptive dynamic inversion
Gai Wendong Wang Honglun Li Dawei.Trajectory tracking for automated aerial refueling based on adaptive dynamic inversion[J].Journal of Beijing University of Aeronautics and Astronautics,2012,38(5):585-590.
Authors:Gai Wendong Wang Honglun Li Dawei
Institution:Science and Technology on Aircraft Control Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Abstract:Receiver aircraft trajectory tracking control is studied during the automated aerial refueling(AAR).The method of the outer guidance loop and inner control loop was adopted.The method was proposed to combine the nonlinear path-following guidance and adaptive neural networks,and was used in receiver aircraft trajectory tracking.The improved nonlinear guidance law was used to guide the receiver aircraft in longitude and latitude.The disturbance and model error were compensated in the angular rates loop.The angular rate error equations with proportional-integral desired dynamics were used to design the update law for the adaptive neural networks.Results show,during the receiver aircraft approaching to the tanker,by using this method,the receiver aircraft ability of restraining disturbance and accommodating model error can be improved effectively,and the control requirement in automated aerial refueling can be met.
Keywords:automated aerial refueling  nonlinear guidance  trajectory tracking  adaptive neural networks
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