Autonomous Shape Decision Making of Morphing Aircraft with Improved Reinforcement Learning

Abstract

The autonomous shape decision-making problem of a morphing aircraft (MA) with a variable wingspan and sweep angle is studied in this paper. Considering the continuity of state space and action space, a more practical autonomous decision-making algorithm framework of MA is designed based on the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the DDPG with a task classifier (DDPGwTC) algorithm is proposed in combination with the long short-term memory (LSTM) network to improve the convergence speed of the algorithm. The simulation results show that the shape decision-making algorithm based on the DDPGwTC enables MA to adopt the optimal morphing strategy in different task environments with higher autonomy and environmental adaptability, which verifies the effectiveness of the proposed algorithm.

Keywords

• morphing aircraft
• reinforcement learning
• deep deterministic policy gradient
• long short-term memory network
• shape decision making