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摘要: 针对无人机集群系统, 提出了一种性能函数引导的深度强化学习控制方法, 同时评估性能函数的示范经验与学习策略的探索动作, 保证了高效可靠的策略更新, 实现了无人机集群系统的高性能控制. 首先, 利用领航-跟随集群框架, 将无人机集群的控制问题转化为领航-跟随框架下的跟踪问题, 进而提出了基于模型的跟踪控制方法, 利用性能函数将集群编队误差约束在给定范围内, 实现了无人机集群的模型驱动控制. 接下来, 为了解决复杂工况下性能函数极易失效难题, 将深度强化学习方法和性能函数驱动方法结合, 提出了性能函数引导的深度强化学习控制方法, 利用性能函数的示范经验辅助训练强化学习网络, 通过同时评估探索与示范动作, 保证学习策略显著优于性能函数驱动控制方法, 有效提高了无人机编队控制精度与鲁棒性. 实验结果表明, 该方法能够显著提升无人机集群的控制精度, 实现了兼顾鲁棒性与飞行精度的高性能集群控制.Abstract: In this paper, a novel performance function-guided deep reinforcement learning control method is proposed for the UAV swarm system, where the demonstration of performance function and deep reinforcement learning exploration are both utilized to guarantee efficient and reliable policy updating, achieving high-performance control of the UAV swarm system. Firstly, based on the leader-follower framework, the UAV swarm control problme is formulated as the leader follower tracking problem, and then, the model-based tracking control is proposed, where the performance function is deigsned to constrain the tracking error within a given range, thereby achieving UAV formation. Then, to address the invalid problem of performance function under complex working conditions, the deep reinforcement learning and the performance function-driven methods are combined to propose the performance-function-guided deep reinforcement learning control method, where the demonstration of performance function is used to train the reinforcement learning network, and the learning strategy is significantly better than the performance function-driven control method by simultaneously evaluating exploration and demonstration actions, thereby improving the accuracy and robustness of UAV formation control effectively. Comparative experimental results show that the proposed method significantly improves the control performace of UAV swarms, realizing high-performance swarm control with both robustness and flight accuracy.
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Key words:
- UAV swarm /
- deep reinforcement learning /
- guided learning /
- intelligent formation control
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图 2 性能函数引导的深度强化学习集群控制框架
Fig. 2 Performance-driven cluster control bootstrap policy framework
图 4 无人机集群控制策略训练与测试框架
Fig. 4 Uav cluster control strategy training and testing framework
图 3 性能函数驱动的集群控制引导策略框架
Fig. 3 Performance function driven cluster control guidance policy framework
图 9 无人机集群在$ x,\;y,\;z $三个方向的飞行曲线
Fig. 9 The flight curve of the drone cluster in $x $, $y $ and $z $ directions
图 10 无人机集群在$ x,\;y,\;z $三个方向的飞行误差
Fig. 10 Flight error of UAV cluster in $x $, $y $ and $z $ directions
图 11 深度强化学习策略与引导策略在双critic框架中的评价曲线
Fig. 11 Evaluation curves of deep reinforcement learning strategies and guidance strategies in the dual critic framework
参数 值 无人机质量$ \text{m}_i $ $ \text{m}_1 = 1.6\text{kg},\;\text{m}_{2-5} = 1.0 \text{kg} $ 无人机转动惯量$ \mathbb{I}_i $ $ \text{diag}[0.01\,\;\,\;0.01\,\;\,\;0.01]\text{kg}\cdot \text{m}^2 $ 重力加速度 $ 9.8\text{m}/\text{s}^2 $ 学习率$ \lambda_{\alpha_{1,\;2,\;3}} $ $ 1,\;2,\;2\times 10^{-4} $ 训练回合数$ M_\text{max} $ $ 100 $ 训练步数$ N_\text{max} $ $ 500 $ 经验池大小$ \mathcal{B}_{1,\;2} $ $ 10000 $ 采样数据量$ {N_m} $ $ 128 $ 训练折扣因子$ \gamma $ $ 0.95 $ 探索与平滑系数$ \sigma_{1,\;2} $ $ 0.1,\;0.05 $ 控制策略交互频率 $ 100 $ Hz 引导策略参数$ k_{\varphi ij},\;\beta_{ij} $ $ 0.2,\; 0.3 $ 辅助增益矩阵$ K_{pi} $ $ \text{diag}[4\ 4\ 4] $ 辅助增益矩阵$ K_{Ri} $ $ \text{diag}[1.5\ 1.5\ 1.5] $ 外环控制参数$ K_{\zeta i} $ $ \text{diag}[2\ 2\ 2] $ 内环控制参数 $ k_{\eta i} $ $ \text{diag}[1.5\ 1.5\ 1.5] $ 内环控制参数$ k_i $ $ \text{diag}[2\ 2\ 2] $ 
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