摘要
以小展弦比飞翼布局飞机为研究对象,其本体的优化增稳控制器由3个气动舵面(连续控制面)构成.为增加操纵能力,基于神经网络设计了主动涡流(bang-bang型控制面)优化控制器.为加快网络的训练速度和收敛精度,采用三层反馈神经网络,通过改变每层网络权重和隐层神经元单元个数对网络进行优化.最后给出了增加主动涡流控制器的飞机闭环系统的响应结果.与仅采用气动舵面闭环响应结果的对比仿真表明,增加主动涡流控制器后闭环系统可获得更好的响应特性,系统达到稳态的时间和所需气动舵面的控制量均明显减小.
A low aspect ration flying wing aircraft was researched. Full state feedback controller was used for the continuous control effectors to improve the stability of aircraft. To improve the control ability, a vortex flow controller（bang-bang controller） based on three layers BP neural network was used. Through adjust weights for states and the number of hidden layers, the designer could optimize the network. The convergence speed and precision of the network were improved by using variety step network. The evaluation criteria consist of closed-loop system performance and activity level of the vortex flow controller nozzles were given. The resuits show that application of a neural network controller improves general performance of regular design and makes controlling of the vortex flow controller activity more practical comparing to conventional controller.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2008年第6期677-680,共4页
Journal of Beijing University of Aeronautics and Astronautics
基金
“973”国家安全重大基础研究资助项目
新世纪优秀人才支持计划
关键词
涡流控制器
神经网络
小展弦比飞翼
耗散函数
vortex flow controller
neural network
low aspect ratio flying wing
cost function
