Station keeping control for aerostat in wind fields based on deep reinforcement learning
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摘要:
建立了平流层浮空器区域驻留模型，在有动力和无动力推进的情况下，基于马尔可夫决策过程，将具有优先经验回放的双深度Q学习应用于平流层浮空器区域驻留控制。通过平均区域驻留半径、区域驻留有效时间比等参数来评价区域驻留控制方法的效果。典型风场中仿真分析结果指出：在区域驻留半径为50 km、区域驻留时间为3天的任务下，无动力推进的平流层浮空器的平均区域驻留半径为28.16 km，区域驻留有效时间比为83%；有动力推进平流层浮空器的平均区域驻留半径可达8.84 km，可实现区域驻留半径为20 km的飞行控制，区域驻留有效时间比为100%。
Abstract:In this paper, a stratospheric aerostat station keeping model is established. Based on Markov decision process, Double Deep Q-learning with prioritized experience replay is applied to stratospheric aerostat station keeping control under dynamic and non-dynamic conditions. Ultimately, metrics like the average station keeping radius and the station keeping effective time ratio are used to assess the effectiveness of the station keeping control approach. The simulation analysis results show that: under the mission the station keeping radius is 50 km and the station keeping time is three days, in the case of no power propulsion, the average station keeping radius of the stratospheric aerostat is 28.16 km, the station keeping effective time ratio is 83%. In the case of powered propulsion, the average station keeping radius of the stratospheric aerostat is significantly increased. The powered stratospheric aerostat can achieve flight control with a station keeping radius of 20 km, an average station keeping radius of 8.84 km, and a station keeping effective time ratio of 100%.
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图 2 平流层浮空器水平方向转移策略原理
Figure 2. Schematic diagram of the horizontal transfer strategy of the stratospheric aerostat
图 3 基于定点悬停的控制策略流程图
Figure 3. Control strategy flow chart based on fixed-point hovering
图 8 强化学习控制下的飞行仿真结果
Figure 8. Flight simulation results under reinforcement learning control
图 10 东西单通道控制飞行仿真结果
Figure 10. Single-channel control flight simulation results in the east-west direction
图 11 南北单通道控制飞行仿真结果
Figure 11. Single-channel control flight simulation results in the north-south direction
表 1 环境状态空间参数设置
Table 1. Environmental state space parameter setting
参数 取值范围 高度h/km 18～22 东向位置x/km −50～50 北向位置y/km −50～50 副气囊空气质量mair/kg 0～158 风速Sw/(m·s−1) − 风向与位置角度δ 0～π 注：东向、北向位置限制条件为$\sqrt {{x^2} + {y^2}} \leqslant {\text{50}} $，风速Sw根据真实风场确定，风向与位置角度δ根据真实风场与当前位置确定。 
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表 2 无推进系统作用下平流层浮空器动作空间
Table 2. Action space of stratospheric aerostat without propulsion system
动作空间 动作 a1 阀门排气 a2 阀门关 a3 风机吸气
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表 3 东西方向单通道推进系统作用下平流层浮空器动作空间
Table 3. Action space of stratospheric aerostat under the action of single-channel propulsion system in east-west direction
动作空间 动作 a1 阀门排气，螺旋桨向东推进 a2 阀门排气，螺旋桨向西推进 a3 阀门排气，螺旋桨关闭 a4 阀门关，螺旋桨向东推进 a5 阀门关，螺旋桨向西推进 a6 阀门关，螺旋桨关闭 a7 风机吸气，螺旋桨向东推进 a8 风机吸气，螺旋桨向西推进 a9 风机吸气，螺旋桨关闭
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表 4 南北方向单通道推进系统作用下平流层浮空器动作空间
Table 4. Action space of stratospheric aerostat under the action of single-channel propulsion system in north-south direction
动作空间 动作 a1 阀门排气，螺旋桨向北推进 a2 阀门排气，螺旋桨向南推进 a3 阀门排气，螺旋桨关闭 a4 阀门关，螺旋桨向北推进 a5 阀门关，螺旋桨向南推进 a6 阀门关，螺旋桨关闭 a7 风机吸气，螺旋桨向北推进 a8 风机吸气，螺旋桨向南推进 a9 风机吸气，螺旋桨关闭
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表 5 双通道推进系统作用下平流层浮空器动作空间
Table 5. The action space of the stratospheric aerostat under the action of the dual-channel propulsion system
动作空间 动作 a1 阀门排气， 螺旋桨向北 a2 阀门排气， 螺旋桨向东 a3 阀门排气， 螺旋桨向南 a4 阀门排气， 螺旋桨向北 a5 阀门排气，螺旋桨向东北 a6 阀门排气，螺旋桨向东南 a7 阀门排气，螺旋桨向西南 a8 阀门排气，螺旋桨向西北 a9 阀门排气，螺旋桨关闭 a10 阀门关，螺旋桨向北 a11 阀门关，螺旋桨向东 a12 阀门关，螺旋桨向南 a13 阀门关，螺旋桨向西 a14 阀门关，螺旋桨向东北 a15 阀门关，螺旋桨向东南 a16 阀门关，螺旋桨向西南 a17 阀门关，螺旋桨向西北 a18 阀门关，螺旋桨关闭 a19 风机吸气， 螺旋桨向北 a20 风机吸气，螺旋桨向东 a21 风机吸气，螺旋桨向南 a22 风机吸气，螺旋桨向西 a23 风机吸气，螺旋桨向东北 a24 风机吸气，螺旋桨向东南 a25 风机吸气，螺旋桨向西南 a26 风机吸气，螺旋桨向西北 a27 风机吸气，螺旋桨关闭
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表 6 DDQN算法参数设置
Table 6. DDQN algorithm parameter settings
训练参数 数值 批学习数Nb 512 最大训练回合数Nmax 2×104 记忆回放单元大小M 2×106 学习率 0.001 奖励偏差 −0.1 ε-贪婪算法下降参数${\varepsilon _{{\text{dec}}}}$ 0.01 ε初值 0.98
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表 7 平流层浮空器参数
Table 7. Stratospheric aerostat parameters
参数 数值 囊体半径/m 8.7 囊体体积/m3 2780 囊体总质量/kg 48 系统总质量/kg 177.2 阀门数量 1 阀门半径/m 0.04 工作高度/km 18～22
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表 8 平流层浮空器初始状态
Table 8. Initial state of stratospheric aerostat
状态量 状态值 高度${h_0}$/km 20 x方向x0/km 0 y方向y0/km 0 初始空气质量/kg 67.58 初始时间 2021-08-03T0 结束时间 2021-08-06T0
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