Communication Topology Adaptive Control Method for High-speed Space Vehicle Swarms

Aiming at the problems of low robustness and large amount of communication required for the high-speed vehicle swarm control policy based on the traditional communication mechanisms, a swarm control method based on the deep reinforcement learning framework that can independently adjust the number of communications is proposed. A swarm control policy coupled with a control policy and a communication policy is constructed using a deep neural network, and its output includes overload commands to control the movement of the space vehicle and the number of communications with adjacent aircrafts. Through continuous interaction with the task environment, the trained swarm control policy can autonomously adjust the communication topology according to the environment, ensuring the robustness of the swarm control and the low communication traffic of the high-speed vehicle swarm. The simulation results show that, compared with the centralized, hierarchical and distributed communication mechanisms, the proposed adaptive communication mechanism can safely and quickly control the vehicle swarm to reach the target point and maintain the formation topology well under the lower swarm communication traffic. © 2023 China Spaceflight Society. All rights reserved.