A Self-adaptive Region Fuzzy Guidance Law Based on RBF Neural Network for Attacking UAV
In this paper, a three-dimensional (3D) selfadaptive region fuzzy guidance law based on radial basis function (RBF) neural networks for some attacking UAV was proposed. Firstly, 3D motion equations for pursuit-evasion of UAV and maneuvering target are given. Secondly, the proposed method was applied to decreasing the miss distance, which is mostly arisen from the fixed navigation rates of traditional proportional navigation guidance laws (TPNGLs). The line of sight (LOS) rate and the closing speed between the attacking UAV and the target are taken as inputs of the fuzzy controller. The output of the fuzzy controller is the commanded acceleration. Then a nonlinear, self-adaptive region function is introduced based on the RBF neural networks to change the region. This nonlinear function can be changed with the input variables, thus can realize dynamic change of the fuzzy variable region. Finally, two engagement scenarios were examined, and a comparison between TPNGLs and the proposed PNGLRBF was made, the simulation results show that the proposed 3D SRFGLRBF can achieve ideal miss distance than TPNGLs.
fuzzy logic guidance law RBF neural network miss distance maneuvering target
Jinyong Yu Qingjiu Xu Yue Zhi
Department of Control Engineering Naval Aeronautical and Astronautical University Yantai,China
国际会议
上海
英文
426-430
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）