高超声速飞行器末制导段协同避障决策方法

In addressing the issues of cluster control caused by the rapid flight speeds of aerial vehicles and the drastic changes in environmental parameters in the cluster formation control of hypersonic vehicles, a centralized training and distributed execution intelligent cluster control method based on reinforcement learning is proposed. A hypersonic vehicle cluster kinematics model is established, and the observation space and action space are designed by taking the cooperative engagement and formation maintenance as primary objectives, and the overload, communication and obstacles as the constraints. A reward function is designed by taking into account the relationships between the relative positions and speeds of vehicles, vehicle and target, and vehicle and obstacle. The cluster formation control of hypersonic vehicles is achieved by continuously adjusting the weight of the reward function and training the hypersonic vehicles. The evaluation indicators are established to assess the performance of the algorithm, and a large number of simulation analysis in disturbed random environments are made. The results show that the proposed intelligent control method can be used to still complete the formation control in a test environment with increasing the random positions of obstacles, providing a new solution for formation control in high-speed and complex flight environments.