目标加速度未知下的导弹自适应滑模拦截制导

Aiming at the practical problem that it is difficult to obtain the maneuvering acceleration information in the process of maneuvering target interception, an adaptive sliding mode interception guidance law based on RBF neural network is designed, which effectively improves the robustness of missile guidance system. Firstly, combined with the knowledge of spatial geometry, a three-dimensional missile-target relative movement model is constructed. Then, RBF neural network is used to estimate the unknown acceleration of the target effectively, which eliminates the dependence of guidance design on target acceleration information. On this basis, combining with the guidance idea to zero out the line-of-sight angular rate, the adaptive sliding mode guidance law is designed in the pitch plane and yaw plane respectively, and the chattering phenomenon of the system is weakened by the continuous high-gain method, and the normal overload command is given which is more consistent with the missile guidance implementation. The convergence of the proposed method is proved by Lyapunov theorem. Finally, the simulation results in three different interception scenarios show that the proposed sliding mode interception guidance law has high adaptability and robustness to maneuvering targets.