基于DDPG算法的变体飞行器自主变形决策

针对变体飞行器的自主变形决策问题，提出了一种基于深度确定性策略梯度(DDPG)算法的智能二维变形决策方法。以可同时变展长及后掠角的飞行器为研究对象，利用DATCOM计算气动数据，并通过分析获得变形量与气动特性之间关系；基于给定的展长和后掠角变形动力学方程，设计DDPG算法学习步骤；针对对称和不对称变形条件下的变形策略进行学习训练。仿真结果表明:所提算法可以快速收敛，变形误差保持在3%以内，训练好的神经网络提高了变体飞行器对不同飞行任务的适应性，可以在不同的飞行环境中获得最佳的飞行性能。 

Autonomous deformation decision making of morphing aircraft based on DDPG algorithm

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.