目标加速度未知下的导弹自适应滑模拦截制导

针对机动目标拦截过程中加速度信息难以获取的实际问题，设计了一种基于RBF神经网络的自适应滑模拦截制导律，有效提高了导弹制导系统的鲁棒性能。首先，结合空间几何知识，构建了弹-目三维空间相对运动模型；然后，利用RBF神经网络对拦截目标的未知加速度进行了有效估计，消除了制导设计对目标加速度信息的依赖性；在此基础上，结合零化视线角速率的制导理念，分别在导弹俯仰平面和偏航平面设计了对应的自适应滑模制导律，同时采用连续高增益法削弱了系统的抖振现象，并给出更符合导弹制导实现的法向过载指令，利用Lyapunov定理证明了所提方法的收敛性；最后，通过仿真验证，在三种不同的拦截场景下进行了仿真对比，仿真结果表明所提滑模拦截制导律对目标机动有较高的自适应性和鲁棒性。

Adaptive Sliding Mode Interception Guidance for the Missile with Unknown Target Acceleration

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.