摘要
首先给出理想情况下固定翼无人机的姿态动力学模型,然后在此基础上根据动态逆方法设计出无人机姿态控制器,但基于此控制器并不适合实际情况下的无人机模型,原因在于无人机实际模型与理想模型之间存在一定的偏移,即模型不确定,而且在实际情况中还存在一定的干扰,因此,在原有无人机姿态控制器的基础上结合模糊神经网络,来补偿无人机运行过程中存在的模型不确定及干扰。
Firstly the attitude dynamic model of the fixed-wing UAV under ideal conditions is given and then the UAV attitude controller is designed based on the dynamic inverse method.But the controller above is not suitable for the UAV model under actual conditions because there is a certain offset between the actual model and the ideal model which is model uncertainty.Moreover there are also some external disturbances in the actual situation.Therefore the fuzzy neural network is combined with the original UAV attitude controller to compensate for the model uncertainty and external disturbances existed in the operation of the UAV.
作者
唐余
林达
曹立佳
刘永春
TANG Yu;LIN Da;CAO Lijia;LIU Yongchun(Sichuan University of Science&Engineering,School of Automation and Information Engineering,Yibin 644000 China;Sichuan University of Science&Engineering,School of Physics and Electronics Engineering,Yibin 644000 China;School of Information and Electronics Engineering Xuzhou University of Technology,Xuzhou 221111 China)
出处
《电光与控制》
CSCD
北大核心
2020年第1期85-89,共5页
Electronics Optics & Control
基金
人工智能四川省重点实验室基础研究重点项目(2016RZ J02)
四川省科技计划项目(19ZDZX0037,2017GZ0303)
四川省智慧旅游研究基地规划项目(ZHZJ18-01)
四川理工学院人才引进项目(2018RCL18)
校级研究生创新基金资助项目(y2018037)
徐州工程学院培育项目(XKY2018126)
关键词
固定翼无人机
模型不确定性
外部干扰
动态逆
模糊神经网络
fixed-wing UAV
model uncertainty
external disturbance
dynamic inversion
fuzzy neural network
