基于DDPG算法的变体飞行器自主变形决策

An intelligent 2D deformation decision method based on deep deterministic policy gradient (DDPG) algorithm is proposed for the autonomous deformation decision making of morphing aircraft. The vehicle that can change at the same time the span length and sweepback is taken as the research object, DATCOM is used to calculate the aerodynamic data, and through the analysis, the relation between deformation and aerodynamic characteristics is obtained. DDPG algorithm learning steps are designed based on the given span length and sweepback deformation dynamics equation. The deformation strategy under the condition of symmetrical and asymmetrical deformation is learned and used to train. The simulation results show that the proposed algorithm can achieve fast convergence, and the deformation error is kept within 3%. The trained neural network improves the adaptability of the morphing aircraft to different flight missions, and the optimal flight performance can be obtained in different flight environments.