The invention relates to a ship rolling motion attitude control system based on multiple learning algorithms. The ship rolling motion attitude control system mainly comprises a rolling detection device, a ship rolling motion controller, a fin servo system and a database, wherein the ship rolling motion controller comprises a rolling deep learning neural network identifier and a rolling deep learning neural network sub-controller, and historical rolling data is stored in the database; the rolling detection device is used for detecting target rolling data generated in the moving process of the target ship; the ship rolling motion controller is used for generating a target fin control signal according to the target rolling data and sending the target fin control signal to the fin servo systemto control the motion of the single fin so as to control the target ship; the historical rolling data is determined by a ship rolling data classification model; and the ship rolling data classification model is constructed on the basis of an unsupervised machine learning algorithm. According to the invention, a deep learning neural network method and an unsupervised machine learning method are combined, and the ship rolling motion attitude control effect is effectively improved.
本发明涉及一种基于多种学习算法的船舶横摇运动姿态控制系统,主要包括横摇检测装置、包括横摇深度学习神经网络辨识器和横摇深度学习神经网络子控制器的船舶横摇运动控制器、鳍伺服系统及存储有历史横摇数据的数据库;横摇检测装置用于检测目标船舶在运动过程中产生的目标横摇数据;船舶横摇运动控制器用于根据所述目标横摇数据生成目标鳍控制信号,并发送至鳍伺服系统以控制单体鳍的运动进而实现对目标船舶的控制;历史横摇数据是船舶横摇数据分类模型确定的;船舶横摇数据分类模型是在无监督机器学习算法的基础上构建得到的。本发明将深度学习神经网络方法与无监督机器学习方法相结合,有效提高船舶横摇运动姿态控制效果。
Ship rolling motion attitude control system based on multiple learning algorithms
一种基于多种学习算法的船舶横摇运动姿态控制系统
2020-09-04
Patent
Electronic Resource
Chinese
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