A unified intelligent control strategy synthesizing multi-constrained guidance and avoidance penetration

We propose an intelligent control strategy that synthesizes optimal guidance and SAC, guidance and NFZs avoidance missions are realized with high precision and low energy loss. The training efficiency is enhanced by introducing a prediction method to calculate the terminal states and adding process rewards. By improving the training process, the learned strategy has a strong generalization on problems of dynamic NFZs, indicating higher applicability and flexibility in flight missions.