基于深度强化学习的无人机编队控制

UAV Formation Control Based on Deep Reinforcement Learning

针对无人机编队控制中无人机智能化程度不足、缺乏自主学习能力等问题,基于深度强化学习中DDQN算法设计无人机编队控制器,该控制器可同时控制速度与航向通道,使僚机能够自学习跟踪长机并保持编队,提高无人机智能化程度。为验证设计控制器的有效性,通过仿真将设计的DDQN控制器与传统PID控制器进行对比。对比结果表明,该控制器可有效形成无人机编队并满足编队要求,对无人机编队智能化控制进行了有效探索。

The UAV formation control has such problems as insufficient intelligence level and lack of self-learning ability.Aiming at the problems a UAV formation controller based on DDQN algorithm in deep reinforcement learning is designed.The controller can control the speed and heading channels simultaneously so that the followers can track the leader and maintain the formation through self-learning and the intelligence level of the UAV is improved.To verify the effectiveness of the designed controller the traditional PID controller is used for comparison.The simulation results show that the DDQN-based controller can effectively create UAV formation and meet the requirements of UAV formation.The study is an effective exploration for intelligent control of UAV formation.