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论文:2024,Vol:42,Issue(3):377-385 |
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引用本文： |
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秦朋, 罗建军, 马卫华, 武黎明. 基于神经核网络高斯过程回归的甲板运动预测[J]. 西北工业大学学报 |
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QIN Peng, LUO Jianjun, MA Weihua, WU Liming. Deck motion prediction using neural kernel network Gaussian process regression[J]. Journal of Northwestern Polytechnical University |
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| 基于神经核网络高斯过程回归的甲板运动预测 |
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| 秦朋, 罗建军, 马卫华, 武黎明 |
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| 西北工业大学 航天学院, 陕西 西安 710072 |
| 摘要: |
| 甲板运动预测与补偿是舰载机自动着舰的关键技术之一。传统甲板运动预测方法依赖于运动建模的准确性和参数调整,面临复杂海况、不同舰型、航态变化时具有适应性差、预测时长短、结果可靠性低等问题。提出一种基于神经核网络高斯过程回归(NKN-GPR)的甲板运动预测模型,使用神经核网络(NKN)实现高斯过程回归(GPR)模型自动复合核构造,有效改善基于规则库自动核搜索(ACKS)算法依赖人工先验知识的不足。以正弦波组合模型和功率谱模型构造仿真数据,对NKN-GPR模型和基于最小二乘法的自回归(AR)模型进行对比仿真验证,仿真结果表明,NKN-GPR模型在运动预测精度、平滑性、预测时长等方面具有显著优势,证明了所提算法的有效性,可为舰载机自动安全着舰提供理论支撑。 |
| 关键词:
自动着舰
甲板运动预测
高斯过程回归
神经核网络
自动复合核构造
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| Deck motion prediction using neural kernel network Gaussian process regression |
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| QIN Peng, LUO Jianjun, MA Weihua, WU Liming |
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| School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| Deck motion prediction and compensation are critical technologies for carrier-based aircraft automatic landing. Traditional deck motion prediction methods rely on precision of motion models and parameter adjustments, facing challenges in adaptability to complex sea conditions, different types of carriers, changes in flight conditions, and limitations in prediction duration, as well as reliability issues. This paper proposes a deck motion prediction method based on the neural kernel network Gaussian process regression (NKN-GPR) model. The NKN-GPR model can utilize a neural kernel network (NKN) to automatically construct the Gaussian process regression (GPR) model's composite kernel, effectively addressing the limitations of the automated kernel search (ACKS) algorithm, which heavily depends on manual prior knowledge. Simulation data is generated using a combination of sine wave and power spectrum models, and the NKN-GPR model is compared with an autoregressive (AR) model based on least squares in a simulated validation. The simulation results demonstrate that the NKN-GPR model exhibits significant advantages in motion prediction accuracy, smoothness, and prediction duration, which confirms the effectiveness of the proposed algorithm. This study provides theoretical support for safe automatic landing of carrier-based aircraft. |
| Key words:
automatic carrier landing
deck motion prediction
Gaussian process regression
neural kernel networks
automatic composite kernel construction
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| 收稿日期: 2023-06-10
修回日期:
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| DOI: 10.1051/jnwpu/20244230377 |
| 基金项目: 国家自然科学基金(12072269)资助 |
| 通讯作者: 罗建军(1965—),教授 e-mail:jjluo@nwpu.edu.cn
Email：jjluo@nwpu.edu.cn |
| 作者简介: 秦朋(1990—),硕士研究生
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参考文献: |
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[1] 朱齐丹, 张智, 张雯. 航母舰载机安全起飞、着舰技术[M]. 哈尔滨: 哈尔滨工程大学出版社, 2016 ZHU Qidan, ZHANG Zhi, ZHANG Wen. Aircraft carrier aircraft safe takeoff and landing technology[M]. Harbin: Harbin Engineering University Press, 2016(in Chinese)
[2] SUN Q, TANG Z, GAO J P, ZHANG G C. Short-term ship motion attitude prediction based on LSTM and GPR[J]. Applied Ocean Research, 2022, 118: 102927
[3] KAPLAN P. A study of prediction techniques for aircraft carrier motions at sea[C]//AIAA 6th Aerospace Sciences Meeting, 1968: 68-123
[4] YUMORI I. Real time prediction of ship response to ocean waves using time series analysis[C]//Proceedings of OCEANS 81, 1981: 1082-1089
[5] JIANG H, DUAN S L, HUANG L, et al. Scale effects in AR model real-time ship motion prediction[J]. Ocean Engineering, 2020, 203: 107202
[6] KHAN A, BIL C, MARION K E. Ship motion prediction for launch and recovery of air vehicles[C]//Proceedings of OCEANS 2005 MTS/IEEE, 2005: 2795-2801
[7] LUO W, REN J. On the identification of coupled pitch and heave motions using support vector machine[C]//2016 Chinese Control and Decision Conference, 2016: 3316-3321
[8] YIN J, ZOU Z, XU F. On-line prediction of ship roll motion during maneuvering using sequential learning RBF neural networks[J]. Ocean Engineering, 2013, 61: 139-147
[9] SILVA K M, MAKI K J. Data-driven system identification of 6-DoF ship motion in waves with neural networks[J]. Applied Ocean Research, 2022, 125: 103222
[10] XU C Z, ZOU Z J. Online prediction of ship roll motion in waves based on auto-moving gird search-least square support vector machine[J]. Mathematical Problems in Engineering, 2021, 2021: 2760517
[11] SKULSTAD R, LI G, FOSSEN T, et al. A cooperative hybrid model for ship motion prediction[J]. Modeling Identification and Control, 2021, 42(1): 17-26
[12] 张彪, 彭秀艳, 高杰. 基于ELM-EMD-LSTM组合模型的舰船运动姿态预测[J]. 舰船力学, 2020, 24(11): 1413-1421 ZHANG Biao, PENG Xiuyan, GAO Jie. Ship motion attitude prediction based on ELM-EMD-LSTM integrated model[J]. Journal of Ship Mechanics, 2020, 24(11): 1413-1421(in Chinese)
[13] ZHANG G, TAN F, WU Y. Ship motion attitude prediction based on an adaptive dynamic particle swarm optimization algorithm and bidirectional LSTM neural network[J]. IEEE Access, 2020, 8: 90087-90098
[14] NIE Z, SHEN F, XU D, et al. An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect[J]. Ocean Engineering, 2020, 217: 107927
[15] 何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129 HE Zhikun, LIU Guangbin, ZHAO Xijing, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129(in Chinese)
[16] 余敏, 罗建军, 王明明. 基于机器学习的空间翻滚目标实时运动预测[J]. 航空学报, 2021, 42(2): 195-205 YU Min, LUO Jianjun, WANG Mingming. Real-time motion prediction of space tumbling targets based on machine learning[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(2): 195-205(in Chinese)
[17] DUVENAUD D, LLOYD J, GROSSE R, et al. Structure discovery in nonparametric regression through compositional kernel search[C]//Proceedings of the 30th International Conference on Machine Learning, PMLR, 2013: 1166-1174
[18] RICHARDSON R R, OSBORNE M A, HOWEY D A. Gaussian process regression for forecasting battery state of health[J]. Journal of Power Sources, 2017, 357: 209-219
[19] 胡伟杰, 黄增辉, 刘学军, 等. 基于自动核构造高斯过程的导弹气动性能预测[J]. 航空学报, 2021, 42(4): 289-302 HU Weijie, HUANG Zenghui, LIU Xuejun, et al. Missile aerodynamic performance prediction of Gaussian process through automatic kernel construction[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 289-302(in Chinese)
[20] SUN S, ZHANG G, WANG C, et al. Differentiable compositional kernel learning for Gaussian processes[C]//Proceedings of the 35th International Conference on Machine Learning, PMLR, 2018: 4828-4837
[21] 王可, 徐明亮, 李亚飞, 等. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型[J]. 自动化学报, 2021, 48(1): 1-9 WANG Ke, XU Mingliang, LI Yafei, et al. A robust learning model for deck motion prediction of aircraft carrier[J]. Acta Automatica Sinica, 2021, 48(1): 1-9(in Chinese)
[22] SCHULZ E, SPEEKENBRINK M, KRAUSE A. A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions[J]. Journal of Mathematical Psychology, 2018, 85: 1-16
[23] WILSON A, ADAMS R. Gaussian process kernels for pattern discovery and extrapolation[C]//Proceedings of the 30th International Conference on Machine Learning, PMLR, 2013: 1067-1075
[24] 杨一栋, 余俊雅. 舰载飞机着舰引导与控制[M]. 北京: 国防工业出版社, 2007 YANG Yidong, YU Junya. Shipboard aircraft landing guidance and control[M]. Beijing: Defense Industry Press, 2007(in Chinese)
[25] HODGES L H, SCHON D A. An analysis of teminal flight path control in carrier landing[R]. AD606040, 1970 |
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