A self-adaptive region fuzzy guidance law based on RBF neural network for attacking UAV

In this paper, a three-dimensional (3D) self-adaptive 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 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.