摘要
为提升舰船航行安全性,提出神经网络优化PID的舰船关键设备智能控制方法。构建PID控制器,将舰船关键设备灵敏度期望值与实际值之间的误差作为控制器输入,经PID控制器控制后,输出使灵敏度误差达到最小的控制结果,将该结果作用于舰船关键设备,实现智能控制。为提升控制效果,采用RBF神经网络优化PID控制器的比例、积分、微分系数3种参数,将优化后的控制器参数作为PID控制器选取最终参数,完成舰船关键设备的智能控制。经实验验证:该方法控制后的舰船蓄能器与伺服阀具有较高的灵敏度,控制后可使发电机在调速时迅速达到相应速度;避免液压缸出现大幅度位移现象。
In order to improve the safety of ship navigation, an intelligent control method of ship key equipment based on neural network optimized PID is proposed. The PID controller is constructed, and the error between the expected value and the actual value of the sensitivity of the ship’s key equipment is taken as the controller input. After being controlled by the PID controller, the control result that minimizes the sensitivity error is output. The result is applied to the ship’s key equipment to achieve intelligent control;In order to improve the control effect, the RBF neural network is used to optimize the three parameters of the PID controller, namely, the proportion, integral and differential coefficients. The optimized controller parameters are used as the final parameters of the PID controller to complete the intelligent control of the key equipment of the ship. The experimental results show that the marine accumulator and servo valve controlled by this method have high sensitivity, and the generator can quickly reach the corresponding speed when adjusting the speed;Avoid large displacement of hydraulic cylinder.
作者
常志东
CHANG Zhi-dong(Weihai Ocean Vocational College,Rongcheng 264300,China)
出处
《舰船科学技术》
北大核心
2022年第21期168-171,共4页
Ship Science and Technology
