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. 仿真实验表明SymTAL-POP在轨迹跟踪误差上表现突出, 平均值降低23.5%, 均方根误差减少55.2%.Abstract: To address the problem of unstable attitude control of rotary wing unmanned aerial vehicles (UAVs) in complex sea breeze environments, the attitude stabilization method named SymTAL-POP (symmetric temporal adversarial learning-partitioned online prediction) is proposed. The method consists of two parts: offline learning and online prediction. In the offline learning phase, a symmetric temporal domain adversarial adaptive learning algorithm named 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 prediction phase, a wind field prediction model named 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 wind speeds under various wind conditions via a feature selection strategy, improving environmental adaptability of UAVs. Test results 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 show that SymTAL-POP excels in trajectory tracking error, with mean reduction of 23.5% and root mean square error reduction of 55.2%.
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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 N-F 150 $ 0.863\;8 \pm 0.044 $ $ 149.2 \pm 1.37 $ SymTAL 160 $ 0.481\; 8 \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 MSE Mean 0.0653 0.0654 0.0596 0.0618 0.0625 0.0545 0.0616 0.0519 SD 0.0490 0.0490 0.0400 0.0420 0.0460 0.0390 0.0390 0.0280 MAE Mean 0.1999 0.2000 0.1914 0.1955 0.1954 0.1827 0.1954 0.1752 SD 0.0801 0.0804 0.0694 0.0727 0.0762 0.0673 0.0679 0.0508 RMSE Mean 0.2434 0.2435 0.2346 0.2384 0.2388 0.2243 0.2391 0.2194 SD 0.0780 0.0780 0.0670 0.0700 0.0740 0.0650 0.0660 0.0500 MAPE Mean 66.9373 66.1924 61.2980 61.0475 63.0942 51.9177 65.2285 56.0368 SD 23.0140 22.0060 17.5740 17.0630 19.8450 15.6650 19.3100 9.1550 MAXE Mean 0.6491 0.6416 0.6421 0.6444 0.6413 0.6418 0.6481 0.6106 SD 0.1120 0.1100 0.1100 0.1090 0.1090 0.1090 0.1051 0.0960 ARE Mean 0.6163 0.6441 0.6491 0.5558 0.6330 0.6340 0.6116 0.5406 SD 0.1810 0.2020 0.2110 0.0850 0.2020 0.2050 0.1540 0.0780
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表 3 间歇风场算法性能比较
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 MSE Mean 0.0482 0.0514 0.0521 0.0465 0.0530 0.0497 0.0514 0.0449 SD 0.0220 0.0270 0.0340 0.0180 0.0230 0.0230 0.0180 0.0140 MAE Mean 0.1756 0.1723 0.1747 0.1712 0.1748 0.1703 0.1703 0.1574 SD 0.0461 0.0531 0.0569 0.0436 0.0448 0.0448 0.0375 0.0239 RMSE Mean 0.2242 0.2185 0.2216 0.2191 0.2256 0.2154 0.2231 0.1944 SD 0.0470 0.0500 0.0540 0.0440 0.0450 0.0450 0.0410 0.0150 MAPE Mean 66.3108 73.1167 72.4697 68.6956 64.5798 72.0918 59.1044 58.5418 SD 25.3310 32.7970 33.5990 26.1330 24.6470 30.2890 14.5280 12.1350 MAXE Mean 0.7156 0.6843 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 0.0630 ARE Mean 0.6843 0.7312 0.7343 0.6870 0.6744 0.7209 0.5910 0.5854 SD 0.2620 0.3280 0.3480 0.2610 0.2550 0.3030 0.1452 0.1210
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表 4 湍流风场算法性能比较
Table 4 Performance comparison of 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 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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