Abstract:

A reentry guidance method based on long short term memory-deep deterministic policy gradient (LSTM-DDPG) is proposed on the basis of the training framework of the DDPG algorithm to address the problems of poor computational accuracy and insufficient adaptability to strong disturbance conditions in existing DDPG algorithm based reentry guidance methods. This method adopts the decoupling design concept of longitudinal and lateral guidance. In terms of longitudinal guidance, firstly, the state and action space required for reinforcement learning are constructed for the reentry guidance problem. Secondly, decision points and instruction calculation strategies within the guidance cycle are determined, and design a reward function that considers comprehensive performance is designed. Then, the LSTM network is introduced to construct a reinforcement learning training network, and multitasking applicability of the algorithms is improved through online updating strategies. Lateral guidance adopts a dynamic bank reversal method based on lateral error to obtain the sign of bank angle. Taking the American general aircraft common aero vehicle-hypersonic (CAV-H) reentry gliding as an example for simulation, the results show that compared with traditional numerical prediction correction methods, the proposed guidance method has significant terminal accuracy and higher computational efficiency advantages. Compared with existing reentry guidance methods based on the DDPG algorithm, the proposed guidance method has considerable computational efficiency, higher terminal accuracy, and robustness.

Key words: reentry gliding guidance, reinforcement learning, deep deterministic policy gradient (DDPG), long short term memory (LSTM) network