再入飞行器深度确定性策略梯度制导方法研究

In order to solve the problem that the traditional reentry vehicle trajectory guidance methods are not adaptable to the strong disturbance conditions and difficult to meet the terminal constraints. Based on the framework of deep deterministic policy gradient (DDPG) reinforcement learning method, conducts network training on the off-line flight trajectory under the random strong disturbance conditions to find the optimal actor network under different environmental conditions. It can be used for guidance trajectory planning under the condition of on-line interference to meet the terminal altitude, range and speed constraints of reentry flight by periodically forecasting the angle of attack and pitch profile of reentry flight. The simulation results show that the maximum terminal residual range deviation is less than 500 m and the maximum terminal speed deviation is less than 35 m/s while meeting the terminal height constraint. Compared with the traditional tracking guidance method, the guidance control method proposed in this paper has higher accuracy and less calculation, which has a good engineering application prospect.