Journal of Astronautics ›› 2017, Vol. 38 ›› Issue (11): 1153-1159.doi: 10.3873/j.issn.1000-1328.2017.11.003

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Deep Reinforcement Learning and Its Application on Autonomous Shape Optimization for Morphing Aircrafts

WEN Nuan, LIU Zheng hua, ZHU Ling pu, SUN Yang   

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  • Received:2017-06-20 Revised:2017-09-13 Online:2017-11-15 Published:2017-11-25


This paper considers a class of simplified morphing aircraft and autonomous shape optimization for aircraft based on deep reinforcement learning is researched. Firstly, based on the model of an abstract morphing aircraft, the dynamic equation of shape and the optimal shape functions are derived. Then, by combining deep learning and reinforcement learning of deterministic policy gradient, we give the learning procedure of deep deterministic policy gradient(DDPG).After learning and training for the deep network, the aircraft is equipped with higher autonomy and environmental adaptability, which will improve its adaptability, aggressivity and survivability in the battlefield. Simulation results demonstrate that the convergence speed of learning is relatively fast, and the optimized aerodynamic shape can be obtained autonomously during the whole flight by using the trained deep network parameters.

Key words: Morphing aircrafts, Deep reinforcement learning, Aerodynamic shape optimization

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