摘 要：舰船在海浪中航行受到海浪、海风等环境因素干扰，不可避免地产生摇摆，给舰船的海上航行造成很大的安全隐患。舰船航行过程中，运动姿态会受到耦合作用、不定周期等诸多因素的干扰，因此很难精确地预测舰船的短期运动姿态。文章提出一种新的预测方法，将机器学习 DBN 网络与 PID 网络相互结合，建立 DBN-PID 模型对舰船的姿态变化进行短期预测。模型有效地解决了舰船运动姿态预测时间短、误差大的问题，具有良好的工程应用价值。
关键词：舰船运动；非线性；短期预测
DOI:10.19850/j.cnki.2096-4706.2021.20.019
中图分类号：TP183 文献标识码：A 文章编号：2096-4706（2021）20-0071-07
Short-term Prediction and Simulation of Ship Motion Attitude Based on DBN-PID Model
CHI Xinpeng, ZHENG Wanghui
(Beijing Institute of Machinery Equipmenth, Beijing 100854, China)
Abstract: Ship navigation in the waves is disturbed by the waves, sea breeze and other environmental factors, inevitably swaying, causing great security risks to the ship navigation. In the process of ship navigation, the motion attitude will be disturbed by many factors, such as coupling effect, indefinite period and so on. Therefore, it is difficult to accurately predict the short-term motion attitude of a ship. In this paper, a new prediction method is proposed, which combines machine learning DBN network with PID network to establish DBNPID model for short-term prediction of ship attitude changes. The model effectively solve the problems of short time and large error of ship motion attitude prediction, and has good engineering application value.
Keywords: ship motion; nonlinearityl; short term forecast
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作者简介：迟鑫鹏（1997.12—），男，汉族，黑龙江肇东人，硕士在读，主要研究方向：发射系统仿真；郑旺辉（1966.03—），男，汉族，湖北崇阳人，研究员，工学硕士，主要研究方向：导弹发射技术与设备。