一种元学习和强化学习结合的多飞行器协同制导律

For the cooperative guidance issue of high-hypersonic re-entry gliding vehicles to simultaneously hit a target at a specified angle in a complex environment,a cooperative guidance law based on meta-learning and reinforcement learning algorithms is proposed. Considering the interference caused by complex combat environment,a Markov decision model for the cooperative guidance issue is established, taking the gliding vehicles’ motion status and proportional guidance factor as the state space and action space. A reward function is designed by comprehensively considering the vehicle-target distance, remaining flight time difference, and overload situation for multiple gliding vehicles attacking a target. Based on meta-learning theory and reinforcement learning algorithm, the proximal policy optimization algorithms are combined with the gated recurrent units to learn the common features of similar cooperative guidance tasks. This approach enhances the accuracy of cooperative guidance strategies in complex interference environments to achieve the constraints on angle of attack and attack time,while also improving the adaptability of cooperative guidance strategy to different combat scenarios. Simulated results indicate that the proposed cooperative guidance law enables multiple aerial vehicles to simultaneously attack a target at a specified attack angle in complex battlefield environment and quickly adapt to new cooperative guidance tasks. The cooperative guidance law maintains good performance even when the cooperative combat scenario changes.