Affiliation:
1. School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Abstract
With the increase of the number of satellites in space, satellite tracking, telemetry, and command (TT&C) is becoming more and more important for aerospace. This paper proposes a method for a low time fragmentation oriented. TT&C task planning method based on Double Deep Q-Network (DDQN). This method mainly solves the problem of poor responses to emergency tasks caused by the large amount of time fragments of equipment under traditional TT&C task-planning methods. Firstly, a multi-objective optimization model aiming at minimizing time fragments and maximizing task revenue is formulated. Then, according to the conflict characteristics of tasks, a task-planning conflict graph is proposed, based on which a TT&C task-planning problem is transferred into an independent set problem. Finally, DDQN is combined with graph structure embedding to solve the transferred independent set problem. The experimental results demonstrate that the proposed method reduces the time fragment of TT&C equipment by 32% and shortens the response time of emergency tasks by 36% compared to existing methods.
Funder
Natural Science Foundation of Shaanxi Province
Young Talent fund of University Association for Science and Technology in Shaanxi China
Key Research and Development Program in Shaanxi Province of China
Postdoctoral Foundation in Shaanxi Province of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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