基于RBF神经网络的舰载稳定平台控制系统  

Control system of shipborne stable platform based on RBF neural network

作　　者：鸦婧 董睿 俞竹青[1] YA Jing;DONG Rui;YU Zhu-qing(School of Mechanical Engineering,Changzhou University,Changzhou 213164,China)

机构地区：[1]常州大学机械与轨道交通学院,江苏常州213164

出　　处：《舰船科学技术》2023年第9期180-185,共6页Ship Science and Technology

摘　　要：舰载稳定平台是一个非线性时变系统,采用传统PID控制难以达到较好的控制性能指标要求。对基于RBF神经网络整定PID参数的控制策略进行研究,针对液压缸突然起动或停止产生的液压冲击问题,根据活塞杆运行距离设计理想速度曲线,建立舰载稳定平台控制系统的数学模型,并在此基础上搭建Simulink仿真模型。仿真结果表明,基于RBF-PID控制的舰载稳定平台较传统PID控制有更快的响应速度、更好的适应能力,在调平开始阶段的超调量降低了16%,到达稳态的时间缩短了1.6s。Shipboard stabilized platform is a nonlinear time-varying systems,the traditional PID control is difficult to achieve good control performance index requirements,to the setting of PID parameters based on the RBF neural networks control strategy are studied,in view of the hydraulic cylinder suddenly start or stop the hydraulic impact problem,the opera-tion of the piston rod from the design ideal speed curve,shipboard stable platform control is established The simulation res-ults show that compared with traditional PID control,the shipborne stable platform based on RBF-PID control has faster re-sponse speed and better adaptability,the overshoot at the beginning of leveling is reduced by 16%,and the time to steady state is shortened by 1.6 s.

关 键 词：舰载稳定平台 RBF神经网络 PID参数整定 理想速度曲线 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]