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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
- DOI:
- 作者:
- 鸦婧, 董睿, 俞竹青
YA Jing, DONG Rui, YU Zhu-qing
- 作者单位:
- 常州大学 机械与轨道交通学院, 江苏 常州 213164
School of Mechanical Engineering, Changzhou University, Changzhou 213164, China
- 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
shipborne stabilized platform; RBF neural network; PID parameter setting; ideal velocity curve
- 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1.6 s。
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 operation of the piston rod from the design ideal speed curve, shipboard stable platform control is established The simulation results show that compared with traditional PID control, the shipborne stable platform based on RBF-PID control has faster response 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.
2023,45(9): 180-185 收稿日期：2022-06-08
DOI：10.3404/j.issn.1672-7649.2023.09.040
分类号：TP273
作者简介：鸦婧(1998-),女,硕士研究生,研究方向为机电一体化、舰载稳定平台研究与开发
