Domain Adversarial Adaptive Learning Based Attitude Stabilization Method for Rotary Wing Unmanned Aerial Vehicles
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摘要: 针对复杂海风环境下旋翼无人机 (Unmanned aerial vehicles, UAVs) 姿态控制不稳定的问题, 提出姿态稳定方法 SymTAL-POP (Symmetric temporal adversarial learning-partitioned online prediction). 该方法包括离线学习和在线预测两个部分. 在离线学习阶段, 引入对称式时序域对抗自适应学习算法 SymTAL. 结合域对抗学习、对称性网络和双向时序网络, SymTAL 有效解决海风环境中无人机姿态稳定问题. 利用深度学习优化加速框架和改进的 Adam 优化器, 提升 SymTAL 学习能力和计算效率. 在线预测阶段, 设计风场预测模型 POP, 实现海风环境实时感知与预测. POP 采用变分模态分解 (Vibration mode decomposition, VMD) 技术处理风速信号, 通过特征选择策略预测不同风况下的风速, 增强无人机环境适应能力. 测试结果表明, SymTAL 在学习效率和控制精度方面均优于其他姿态稳定算法, POP 在连续风、间歇风和湍流风的多风况条件下的预测精度优于其他模型. 仿真实验验证 SymTAL-POP 在轨迹跟踪误差上表现突出, 平均误差降低 23.5%, 均方根误差减少 55%.Abstract: To address unstable attitude control of rotary wing unmanned aerial vehicles (UAVs) in complex sea breeze environments, the SymTAL-POP method(Symmetric Temporal Adversarial Learning-Partitioned Online Prediction) is proposed. It includes offline learning and online prediction. In the offline phase, a symmetric temporal domain adversarial adaptive learning algorithm, SymTAL, is introduced. By combining domain adversarial learning, symmetric networks, and bidirectional temporal networks, SymTAL effectively solves the problem of UAVs attitude stabilization in the sea breeze environments. Utilizing a deep learning optimization acceleration framework and an improved Adam optimizer, the learning capability and computational efficiency of SymTAL are enhanced. In the online phase, wind field prediction model, POP, is designed for real-time sea breeze environment perception and prediction. POP utilizes variational mode decomposition (VMD) technology to process wind speed signals and predicts speeds under various conditions via a feature selection strategy, improving environmental adaptability. Tests show SymTAL outperforms other attitude stabilization algorithms in terms of learning efficiency and control precision, POP exhibits excellent prediction accuracy under multiple wind conditions of continuous, intermittent and turbulent winds. Simulation experiments verify that SymTAL-POP excels in trajectory tracking error, with an average error reduction of 23.5% and a root mean square error reduction of 55%.
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表 1 Loss_f 值分析表
Table 1 Loss_f value analysis table
方法 收敛轮次 (轮) 收敛值 运行时间 (s) Loss_f $ \text{N} $-$ \text{F} $ 150 $ 0.8638 \pm 0.044 $ $ 149.2 \pm 1.37 $ SymTAL 160 $ 0.4818 \pm 0.045 $ $ 116.6 \pm 1.15 $ 
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表 2 连续风场算法性能比较
Table 2 Performance comparison of continuous wind field algorithms
评估指标 cnn - lstm[40] vmd-am-lstm vmd-cnn[41] vmd-cnn-lstm[42] vmd-gru[43] vmd-lstm vmd-tcn-lstm POP(ours) MSE Mean 0.0653 0.0654 0.0596 0.0618 0.0625 0.0545 0.0616 $ {\boldsymbol{0 . 0 5 1 9}} $ SD 0.0490 0.0490 0.0400 0.0420 0.0460 0.0390 0.0390 $ {\boldsymbol{0 . 0 2 80}} $ MAE Mean 0.1999 0.2000 0.1914 0.1955 0.1954 0.1827 0.1954 $ {\boldsymbol{0 . 1 7 5 2}} $ SD 0.0801 0.0804 0.0694 0.0727 0.0762 0.0673 0.0679 $ {\boldsymbol{0 . 0 5 0 8}} $ RMSE Mean 0.2434 0.2435 0.2346 0.2384 0.2388 0.2243 0.2391 $ {\boldsymbol{0 . 2 1 9 4}} $ SD 0.0780 0.0780 0.0670 0.0700 0.0740 0.0650 0.0660 $ {\boldsymbol{0 . 0 5 00}} $ MAPE Mean 66.9373 66.1924 61.2980 61.0475 63.0942 $ {\boldsymbol{5 1 . 9 1 7 7}} $ 65.2285 56.0368 SD 23.0140 22.0060 17.5740 17.0630 19.8450 15.6650 19.3100 $ {\boldsymbol{9 . 1 5 50}} $ MAXE Mean 0.6491 0.6416 0.6421 0.6444 0.6413 0.6418 0.6481 $ {\boldsymbol{0 . 6 1 0 6}} $ SD 0.1120 0.1100 0.1100 0.1090 0.1090 0.1090 0.1051 $ {\boldsymbol{0 . 0 9 60}} $ ARE Mean 0.6163 0.6441 0.6491 0.5558 0.6330 0.6340 0.6116 $ {\boldsymbol{0 . 5 4 0 6}} $ SD 0.1810 0.2020 0.2110 0.0850 0.2020 0.2050 0.1540 $ {\boldsymbol{0 . 0 7 80}} $
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表 3 间歇风风场算法性能比较
Table 3 Performance comparison of intermittent wind field algorithms
评估指标 cnn-lstm[40] vmd-am-lstm vmd-cnn[41] vmd-cnn-lstm[42] vmd-gru[43] vmd-lstm vmd-tcn-lstm POP(ours) MSE Mean 0.0482 0.0514 0.0521 0.0465 0.0530 0.0497 0.0514 $ {\boldsymbol{0 . 0 4 4 9}} $ SD 0.0220 0.0270 0.0340 0.0180 0.0230 0.0230 0.0180 $ {\boldsymbol{0 . 0 1 40}} $ MAE Mean 0.1756 0.1723 0.1747 0.1712 0.1748 0.1703 0.1703 $ {\boldsymbol{0 . 1 5 7 4}} $ SD 0.0461 0.0531 0.0569 0.0436 0.0448 0.0448 0.0375 $ {\boldsymbol{0 . 0 2 3 9}} $ RMSE Mean 0.2242 0.2185 0.2216 0.2191 0.2256 0.2154 0.2231 $ {\boldsymbol{0 . 1 9 4 4}} $ SD 0.0470 0.0500 0.0540 0.0440 0.0450 0.0450 0.0410 $ {\boldsymbol{0 . 0 1 50}} $ MAPE Mean 66.3108 73.1167 72.4697 68.6956 64.5798 72.0918 59.1044 $ {\boldsymbol{5 8 . 5 4 1 8}} $ SD 25.3310 32.7970 33.5990 26.1330 24.6470 30.2890 14.5280 $ {\boldsymbol{1 2 . 1 3 50}} $ MAXE Mean 0.7156 $ {\boldsymbol{0 . 6 8 4 3}} $ 0.6926 0.7032 0.7355 0.7039 0.7537 0.6882 SD 0.1130 0.0970 0.1060 0.1070 0.1080 0.1040 0.1014 $ {\boldsymbol{0 . 0 6 30}} $ ARE Mean 0.6843 0.7312 0.7343 0.6870 0.6744 0.7209 0.5910 $ {\boldsymbol{0 . 5 8 5 4}} $ SD 0.2620 0.3280 0.3480 0.2610 0.2550 0.3030 0.1452 $ {\boldsymbol{0 . 1 2 10}} $
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表 4 湍流风风场算法性能比较
Table 4 Turbulent wind field algorithms
评估指标 cnn-lstm[40] vmd-am-lstm vmd-cnn[41] vmd-cnn-lstm[42] vmd-gru[43] vmd-lstm vmd-tcn-lstm POP(ours) MSE Mean 0.2799 0.2974 0.2760 0.2741 0.2943 0.2966 0.2966 $ {\boldsymbol{0 . 2 4 9 4}} $ SD 0.0950 0.0720 0.0740 0.0820 0.0720 0.0770 0.0770 $ {\boldsymbol{0 . 0 6 90}} $ MAE Mean 0.3963 0.4064 0.4072 0.3965 0.4083 0.4117 0.4038 $ {\boldsymbol{0 . 3 4 8 2}} $ SD 0.0605 0.0581 0.0611 0.0618 0.0616 0.0622 0.0631 $ {\boldsymbol{0 . 0 1 2 5}} $ RMSE Mean 0.5215 0.5283 0.5334 0.5085 0.5346 0.5337 0.5337 $ {\boldsymbol{0 . 4 7 4 2}} $ SD 0.0890 0.0670 0.0720 0.0780 0.0720 0.0730 0.0680 $ {\boldsymbol{0 . 0 4 60}} $ MAPE Mean — — — — — — — — SD — — — — — — — — MAXE Mean 0.8268 0.8610 0.8482 0.8261 0.8760 0.8774 0.8700 $ {\boldsymbol{0 . 7 6 1 4}} $ SD 0.1420 0.1240 0.1310 0.1440 0.1230 0.1300 0.1080 $ {\boldsymbol{0 . 0 7 10}} $ ARE Mean — — — — — — — — SD — — — — — — — —
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表 5 抗风算法的平均位置误差 (cm)
Table 5 The average position error of wind resistance algorithm (cm)
风速 N-T N-MPC INDI L1-A S-P (km/h) 误差 误差 误差 误差 误差 0 10.8 4.5 6.8 4.2 2.9 15 13.6 7.6 8.1 11.1 4.1 30 22.6 11.3 10.3 21.4 8.7 45 34.7 16.7 12.6 28.6 8.9
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表 6 抗风算法可控范围风速等级
Table 6 Wind resistance algorithm controllable range wind speed level
方法 无风
$ (0\sim1 $ 级$ ) $微风
$ (2\sim3 $ 级$ ) $劲风
$ (4\sim5 $级 $ ) $强风
$ (>5 $级$ ) $N-T 中 差 差 中 N-MPC 优 优 中 差 INDI 优 优 中 中 L1-A 优 中 差 差 S-P 优 优 优 中
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表 7 高度与风速的关系
Table 7 Relationship between height and wind speed
高度 $ (\text{m}) $ 风速 $ (\text{m} / \text{s}) $ 10 6.16 20 6.97 30 7.50 50 8.23 60 8.50 70 8.74 80 8.95 90 9.14 100 9.32 110 9.48 120 9.63
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