基于强化学习的高超声速滑翔飞行器自适应末制导

Addressing the uncertainty of dynamic model parameters in the terminal guidance phase of hypersonic gliding vehicles and the slow convergence speed of traditional reinforcement learning algorithm, an adaptive guidance algorithm based on reinforcement learning is proposed. The terminal guidance problem for hypersonic gliding vehicles under nominal conditions is converted into an optimal control problem,which is solved using the sequential convex optimization algorithm to generate a dataset of state-control pairs. The dataset is fitted through supervised learning to obtain a corresponding guidance model. The disturbances such as aerodynamic parameter deviation,uncertainty in control response delay coefficient,and state measurement noise are introduced,and the guidance model is further optimized based on the reinforcement learning framework through numerous interactions between the vehicle and the current environment. Numerically simulated results indicate that the proposed guidance method exhibits better robustness and accuracy compared to the supervised learning guidance method.