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摘要: 航母甲板在风、浪、流等因素影响下做六自由度不规则运动, 影响舰载机着舰精度. 航母甲板运动预估与补偿是自动着舰系统的重要功能之一, 也是提高舰载机着舰安全性与成功率的关键技术之一. 为此, 提出一种面向甲板运动预估的鲁棒学习模型, 通过基本构建单元自适应演化出复杂学习系统. 构建单元的训练采用非梯度的伪逆学习策略, 提高了训练效率, 简化了学习控制超参数调优; 构建单元的架构设计采用数据驱动的策略, 简化了架构超参数调优; 采用图拉普拉斯正则化方法提高了模型对噪声和意外扰动的鲁棒性. 通过某型航母在中等海况条件下以典型航速巡航时的仿真实验, 验证了所提方法在甲板纵摇、横摇以及垂荡运动预估问题中的有效性及鲁棒性.Abstract: The irregular deck motion of the aircraft carrier in six-degree freedom is generally caused by wind, waves, and currents, which affects the precision of aircraft landings. Aircraft carrier deck motion prediction and compensation are important functions of automatic landing systems as well as key technologies improving the safety and success rate of aircraft landing. In this paper, a robust learning model for deck motion prediction was presented, which constructs complex learning systems through the adaptive evolution of basic building blocks. The training of these building blocks employs a non-gradient pseudoinverse learning strategy, which improves training efficiency and simplifies the tuning of learning control hyperparameters. The architecture design of the building blocks adopts a data-driven approach, simplifying architectural hyperparameter tuning. A graph Laplace regularization term was employed in order to enhance the robustness of the model against noise and unexpected perturbations. Through simulation experiments conducted on a specific aircraft carrier cruising at a typical speed under moderate sea conditions, the effectiveness and robustness of the proposed method in predicting the pitch, roll, and heave of the deck are verified.
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Key words:
- Aircraft carrier /
- deck motion prediction /
- robustness /
- machine learning /
- simulation validation
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图 2 多个子模型集成学习系统架构
Fig. 2 The architecture of the ensemble learning system with multiple sub-models
图 7 PILAE 与 PILAE-Lap 的甲板横摇预估结果对比
Fig. 7 The deck roll prediction results comparison between PILAE and PILAE-Lap
图 6 PILAE 与 PILAE-Lap 的甲板纵摇预估结果对比
Fig. 6 The deck pitch prediction results comparison between PILAE and PILAE-Lap
图 8 PILAE与PILAE-Lap的甲板垂荡预估结果对比
Fig. 8 The deck heave prediction results comparison between PILAE and PILAE-Lap
图 9 本文所提方法与其他方法的训练耗时对比
Fig. 9 Training time comparison between our proposed method and others
图 10 本文方法生成的网络架构及运动预估性能
Fig. 10 The network architectures generated by our proposed method and its motion prediction performance
图 11 预估性能与子模型个数的关系
Fig. 11 The prediction performance with different number of sub-model
表 1 本文所提方法与其他方法的预测均方误差对比
Table 1 Comparison of prediction MSE between our proposed method with others
方法 Pitch Roll Heave BPNN 0.021 2 0.016 5 0.075 4 ELM 0.019 8 0.116 5 0.076 5 KELM-PSO 0.012 4 0.013 7 0.056 0 Kalman filter 0.022 4 0.573 7 0.026 1 Autoregression 0.006 6 0.016 8 0.020 8 本文方法 0.001 5 0.025 4 0.002 9 注: 加粗字体表示各列最优结果. 
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