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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2020, Vol. 41 ›› Issue (5): 323630-323630.doi: 10.7527/S1000-6893.2019.23630

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Allocation of composite mode on-orbit service resource based on improved DQN

LIU Bingyan1,2, YE Xiongbing1, ZHOU Chifei1, LIU Biliu2   

  1. 1. Academy of Military Sciences, Beijing 100091, China;
    2. 32032 Troops, Beijing 100094, China
  • Received:2019-11-04 Revised:2019-11-28 Online:2020-05-15 Published:2020-01-10

Abstract: In order to solve the nonlinear multi-objective optimization before on-orbit service, an on-orbit service resource allocation model under the composite service mode is constructed, and an on-orbit service resource allocation method based on Deep Q Network (DQN) convergence and stability improvement was proposed. This approach can cope with a composite service pattern which includes "one to many" and "many to one". This method can prioritize the allocation of important service objects on the premise of satisfying the expected success rate, and at the same time, take into account the comprehensive benefit of resource allocation and the overall energy consumption efficiency, achieving the comprehensive goal of completing the task efficiently and with the expected success rate and less resource input. Simulation results show that improved DQN method can independently allocate spacecraft resources based on the importance of service objects. This method has the advantages of fast convergence, low training error, and obvious comparative advantages in the optimization of distribution benefits and overall energy consumption.

Key words: on-orbit servicing, integer programming, resource allocation, deep reinforcement learning, neural network

CLC Number: