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应用关联规则的半潜式航行体稳态控制技术

Steady-state control technology of semi-submersible vehicle using association rules
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摘要 针对半潜式航行体易受干扰产生稳态误差问题,提出应用关联规则的半潜式航行体稳态控制技术。建立半潜式航行体空间运动数学模型,分析其动力学特性以及地面和流体坐标系间的转换关系,应用关联规则中的Apriori算法,并引入矩阵思想加以改进,挖掘半潜式航行体空间运动数学模型选取控制参数。利用该参数设计一个可调权值的PID控制器,完成半潜式航行体控制。通过卷积神经网络预测获取准确性较高的PID控制器参数,利用该参数不断优化PID控制器,完成半潜式航行体的稳态优化控制。实验结果表明,半潜式航行体的回转半径与垂直舵角呈负相关关系,最大、最小回转半径分别约为100 m和25 m,航行深度同垂直舵角成正比,该方法具有较小的稳态控制超调量和较快的控制响应速度。 The semi submersible vehicle is easy to be disturbed to produce steady-state error. The steady-state control technology of semi submersible vehicle based on association rules is proposed. The mathematical model of space motion of semi submersible vehicle is established, its dynamic characteristics and the transformation relationship between ground and fluid coordinate system are analyzed, the Apriori algorithm in association rules is applied and the matrix idea is introduced to improve it, the mathematical model of space motion of semi submersible vehicle is excavated, the control parameters are selected, and a PID controller with adjustable weight is designed by using this parameter, and the semi submersible vehicle control is completed by using PID controller, The PID controller parameters with high accuracy are obtained by convolution neural network prediction, and the PID controller is continuously optimized by using this parameter to complete the steadystate optimal control of semi submersible vehicle. The experimental results show that the turning radius of semi submersible vehicle is negatively correlated with the vertical rudder angle. The maximum and minimum turning radius are about 100 m and 25 m respectively. The sailing depth is directly proportional to the vertical rudder angle. This method has small steadystate control overshoot and fast control response speed.
作者 万骏 WAN Jun(The 710 Research Institute of CSSC,Yichang 443003,China)
出处 《舰船科学技术》 北大核心 2022年第16期74-78,共5页 Ship Science and Technology
关键词 关联规则 半潜式航行体 稳态控制 PID控制器 在线反馈学习 卷积神经网络 association rules semi-submersible vehicle steady state control PID controller online feedback learning convolutional neural network
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