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一种面向航空母舰甲板运动状态预估的鲁棒学习模型

王可 徐明亮 李亚飞 姜晓恒 鲁爱国 李鉴

王可, 徐明亮, 李亚飞, 姜晓恒, 鲁爱国, 李鉴. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型. 自动化学报, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210664
引用本文: 王可, 徐明亮, 李亚飞, 姜晓恒, 鲁爱国, 李鉴. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型. 自动化学报, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210664
Wang Ke, Xu Ming-Liang, Li Ya-Fei, Jiang Xiao-Heng, Lu Ai-Guo, Li Jian. A robust learning model for deck motion prediction of aircraft carrier. Acta Automatica Sinica, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210664
Citation: Wang Ke, Xu Ming-Liang, Li Ya-Fei, Jiang Xiao-Heng, Lu Ai-Guo, Li Jian. A robust learning model for deck motion prediction of aircraft carrier. Acta Automatica Sinica, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210664

一种面向航空母舰甲板运动状态预估的鲁棒学习模型

doi: 10.16383/j.aas.c210664
基金项目: 国家自然科学基金 (62036010), 中国博士后科学基金 (2020M682348), 河南省高等学校重点科研项目计划 (21A520002), 国家自然科学基金 (61972362), 河南省自然科学基金 (202300410378), 国家自然科学基金 (61802351)资助
详细信息
    作者简介:

    王可:郑州大学计算机与人工智能学院讲师. 研究方向为基于计算智能的优化与学习

    徐明亮:郑州大学计算机与人工智能学院教授. 主要研究方向为计算机图形学,人工智能. 本文通讯作者. E-mail: iexumingliang@zzu.edu.cn

    李亚飞:郑州大学计算机与人工智能学院教授. 主要研究方向为群体智能与机器学习

    姜晓恒:郑州大学计算机与人工智能学院副教授. 主要研究方向为深度学习,机器视觉

    鲁爱国:武汉数字工程研究所(709所)研究员. 主要研究方向为信息系统与软件,人机交互

    李鉴:武汉数字工程研究所(709所)研究员. 主要研究方向为信息系统与软件

A Robust Learning Model for Deck Motion Prediction of Aircraft Carrier

Funds: Supported by National Natural Science Foundation of P. R. China (62036010), China Postdoctoral Science Foundation (2020M682348), Key Research Foundation of Henan Higher Education Institutions (21A52002), National Natural Science Foundation of P. R. China (61972362), Natural Science Foundation of Henan Province (202300410378), National Natural Science Foundation of P. R. China (61802351)
More Information
    Author Bio:

    WANG Ke Lecturer at School of Computer and Artificial Intelligence, Zhengzhou University. His research interest covers computational intelligence based optimization and learning

    XU Ming-Liang Professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interest covers computer graphics and artificial intelligence. Corresponding author of this paper

    LI Ya-Fei Professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interest covers swarm intelligence and machine learning

    JIANG Xiao-Heng Associate professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interest covers deep learning and computer vision

    LU Ai-Guo Professor at Wuhan Digital Engineering Institute (No. 709 Research Institute). His research interest covers information system and software, human-computer interaction

    LI Jian Professor at Wuhan Digital Engineering Institute (No. 709 Research Institute). His research interest covers information System and Software

  • 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. 本文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.
  • 图  1  舰船平移运动及摇荡运动

    Fig.  1  The translational motion and swaying motion of a ship

    图  2  多网络集成学习系统架构

    Fig.  2  The architecture of the ensemble learning system with multiple sub-models

    图  3  不同信噪比下的甲板纵摇预估结果

    Fig.  3  The prediction result of deck pitch with different SNR

    图  5  不同信噪比下的甲板横摇预估结果

    Fig.  5  The prediction result of deck roll with different SNR

    图  7  不同信噪比下的甲板垂荡预估结果

    Fig.  7  The prediction result of deck heave with different SNR

    图  4  PILAE与PILAE-Lap的甲板纵摇预估结果对比

    Fig.  4  he deck pitch prediction result comparison between PILAE and PILAE-Lap

    图  6  PILAE 与 PILAE-Lap 的甲板横摇预估结果对比

    Fig.  6  The deck roll prediction result 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 result comparison between PILAE and PILAE-Lap

    图  9  本文所提方法与其它方法的训练耗时对比

    Fig.  9  Training time comparison between our 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 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  本文所提方法与其它方法的均方误差对比

    Table  1  Comparison of prediction MSE between our proposed method with others

    MethodsPitchRollHeave
    BPNN0.02120.01650.0754
    ELM0.01980.11650.0765
    KELM-PSO0.01240.01370.0560
    Kalman filter0.02240.57370.0261
    Autoregression0.00660.01680.0208
    Ours0.00150.02540.0029
    下载: 导出CSV
  • [1] 甄子洋. 舰载无人机自主着舰回收制导与控制研究进展. 自动化学报, 2019, 45(4): 669-681

    Zhen Zi-Yang. Research development in autonomous carrier-landing/ship-recovery guidance and control of unmanned aerial vehicles. Acta Automatica Sinica, 2019, 45(4): 669-681
    [2] 石明, 屈香菊, 王萌辉. 甲板运动对舰载机人工着舰的影响和补偿. 飞行力学, 2006, 24(1): 5-8 doi: 10.3969/j.issn.1002-0853.2006.01.002

    SHI Ming, QU Xiang-Ju, WANG Meng-Hui The influence and compensation of deck motion in carrier landing approach. Flight Dynmics, 2006, 24(1): 5-8 doi: 10.3969/j.issn.1002-0853.2006.01.002
    [3] 张志冰, 甄子洋, 江驹, 薛艺璇. 舰载机自动着舰引导与控制综述. 南京航空航天大学学报, 2018, 50(6): 734-744

    ZHANG Zhi-Bing, ZHEN Zi-Yang, JIANG Ju, XUE Yi-Xuan. Review on Development in Guidance and control of automatic carrier landing of carrier-based aircraft. Journal of Nanjing University of Aeronautics & Astronautics, 2018, 50(6): 734-744
    [4] 江驹, 王新华, 甄子洋, 杨一栋, 袁锁中, 周鑫. 舰载机起飞着舰引导与控制. 北京: 科学出版社, 2019.

    JIANG Ju, WANG Xin-Hua, ZHEN Zi-Yang, YANG Yi-Dong, YUAN Suo-Zhong, ZHOU Xin. Guidance and Control of Carrier-Based Aircraft Launching and Landing, Beijing: Science Press, 2019.
    [5] 王能建, 刘钦辉, 李江, 商振. 舰载机出动回收能力仿真研究. 北京: 科学出版社, 2018.

    WANG Neng-Jian, LIU Qin-Hui, LI Jiang, SHANG Zhen. Simulation on ircraft Sortie Generation Rate. Beijing: Science Press, 2018.
    [6] 张永花, 周鑫. 舰载机着舰点垂直运动补偿技术仿真研究. 系统仿真学报, 2013, 25(04): 826-830

    ZHANG Yong-Hua, ZHOU Xin. Simulation study on landing point vertical motion in carrier landing. Journal of System Simulation. 2013, 25(04): 826-830
    [7] 周鑫, 彭荣鲲, 袁锁中. 舰载机理想着舰点垂直运动的预估与补偿. 航空学报, 2013, 34(7): 1663-1669

    ZHOU Xin, PENG Rong-Kun, YUAN Suo-Zhong. Prediction and compensation for vertical motion of ideal touchdown point in carrier landing. Acta Aeronautica ET Astronautica Sinica, 2013, 34(7): 1663-1669
    [8] Xue Y X, Zhen Z Y, Yang L Q, Wen L D. Adaptive fault-tolerant control for carrier-based UAV with actuator failures. Aerospace Science and Technology, 2020, 107: 106227. doi: 10.1016/j.ast.2020.106227
    [9] Nicolau V, Aiordachioaie D, Popa R. Neural network prediction of the wave influence on the yaw motion of a ship. In: Proceedings of IEEE International Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2004. 2801−2806
    [10] Liu Xixiang, Wang Qiming, Huang Yongjiang, Song Qing, Zhao Liye. A prediction method for deck motion of aircraft carrier based on particle swarm optimization and kernel extreme learning machine. Sensors and Materials, 2017, 29(9): 1291-1303
    [11] Li Guoyuan, Kawan B, Wang Hao, Zhang Houxiang. Neural-network-based modelling and analysis for time series prediction of ship motion. Ship Technology Research, 2017, 64(1): 30-39 doi: 10.1080/09377255.2017.1309786
    [12] Sidar M, Doolin B. On the feasibility of real-time prediction of aircraft carrier motion at sea. IEEE Transactions on Automatic Control, 1983, 28(3): 350-356 doi: 10.1109/TAC.1983.1103227
    [13] 邢伯阳, 潘峰, 王位, 冯肖雪. 基于复合地标导航的动平台四旋翼飞行器自主优化降落技术. 航空学报, 2019, 40(6): 322601−322601

    XING Bo-Yang, PAN Feng, WANG Wei, FENG Xiao-Xue. Moving platform self-optimization landing technology for quadrotor based on hybrid landmark. 2019, 40(6): 322601−322601
    [14] Bhatia A K, Ju J, Kumar A, Shah S, Zhen Z Y. Adaptive preview control with deck motion compensation for autonomous carrier landing of an aircraft. International Journal of Adaptive Control Signal Processing, 2021, 35(5): 769-785 doi: 10.1002/acs.3228
    [15] Zhen Ziyang, Jiang Shuoying, Ma Kun. Automatic carrier landing control for unmanned aerial vehicles based on preview control and particle filtering. Aerospace Science and Technology, 2018, 81: 99-107 doi: 10.1016/j.ast.2018.07.039
    [16] Zhen Ziyang, Jiang Shuoying, Jiang Ju. Preview control and particle filtering for automatic carrier landing. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(6): 2662-2674 doi: 10.1109/TAES.2018.2826398
    [17] 杨柳, 徐东昊. 基于极短期运动预报的舰载机着舰过程仿真分析. 中国舰船研究, 2018, 13(4): 99-103

    YANG Liu, XU Dong-Hao. Aircraft carrier landing process simulation based on extremely short-term prediction of ship motion. Chinese Journal of Ship Research. 2018, 13(4): 99-103
    [18] Yin Jianchuan, Zou Zaojian, Xu Feng, Wang Nini. Online ship roll motion prediction based on grey sequential extreme learning machine. Neurocomputing, 2014, 129(10): 168-174.
    [19] Wang Ke, Guo Ping, Xin Xin, Ye Zebin. Autoencoder, low rank approximation and pseudoinverse learning algorithm. In: Proceedings of 2017 IEEE International Conference on Systems, Man, and Cybernetics. Banff, AB, Canada: IEEE, 2017. 948−953.
    [20] Guo Ping, Wang Ke, Zhou Xiuling. PILAE: A non-gradient descent learning scheme for deep feedforward neural networks[Online], available: https://arxiv.org/abs/1811.01545v3, November 9, 2021
    [21] Wang Ke, Guo Ping. An ensemble classification model with unsupervised representation learning for driving stress recognition using physiological signals. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(6): 3303-3315 doi: 10.1109/TITS.2020.2980555
    [22] Guo Ping, Lyu M R. A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data. Neurocomputing, 2004, 56(1): 101-121.
    [23] Rifai S, Vincent P, Muller X, Glorot X, Bengio Y. contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of 28th International Conference on Machine Learning. Bellevue, Washington, USA: Omnipress, 2011. 833−840
    [24] Wang Ke, Guo Ping. A robust automated machine learning system with pseudoinverse learning. Cognitive Computation, 2021, 13(3): 724-735. doi: 10.1007/s12559-021-09853-6
    [25] Diallo B, Hu Jie, Li Tianru, Khan G A, Liang Xinyan, Zhao Yimiao. Deep embedding clustering based on contractive autoencoder. Neurocomputing, 2021, 433: 96-107. doi: 10.1016/j.neucom.2020.12.094
    [26] Wu E Q, Peng X Y, Zhang C Z, Lin J X, Sheng R S F. Pilots’ fatigue status recognition using deep contractive autoencoder network. IEEE Transactions on Instrumentation and Measurement, 2019, 68(10): 3907-3919. doi: 10.1109/TIM.2018.2885608
    [27] 陈晓云, 陈媛. 子空间结构保持的多层极限学习机自编码器. 自动化学报, 2021, x(x): 1-14

    Chen Xiao-Yun, Chen Yuan. Multi-layer extreme learning machine autoencoder with subspace structure preserving. Acta Automatica Sinica, 2021, x(x): 1-14
    [28] 张万栋, 李庆忠, 黎明, 武庆明. 基于最优误差自校正极限学习机的高频地波雷达RD谱图海面目标检测算法. 自动化学报, 2021, 47(1): 108-120

    Zhang Wan-Dong, Li Qing-Zhong, Li Ming, Q. M. Jonathan Wu. Sea surface target detection for RD images of HFSWR based on optimized error self-adjustment extreme learning machine. Acta Automatica Sinica, 2021, 47(1): 108-120
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