| 张继红.基于RBF神经网络滑模控制的卷纸纠偏系统[J].中国造纸学报,2024,39(1):107-113 |
二维码（扫一下试试看！） |
| 基于RBF神经网络滑模控制的卷纸纠偏系统 |
| Research on Paper Roll Deviation Control System Based on RBF Neural Network Sliding Module Control |
| 投稿时间：2023-02-14 修订日期：2023-04-09 |
| DOI：10.11981/j.issn.1000-6842.2024.01.107 |
| 中文关键词: 卷纸 纠偏控制 RBF神经网络 滑模控制 MATLAB/Simulink 动态性能 |
| Key Words:paper roll deviation control RBF neural network sliding module control MATLAB/Simulink dynamic performance |
| 基金项目:四川省教育厅自然科学科研项目（21ZA0347）。 |
|
| 摘要点击次数: 1309 |
| 全文下载次数: 768 |
| 中文摘要: |
| 设计了采用RBF神经网络控制的伺服纠偏控制系统，通过建立其动力学模型，运用MATLAB/Simulink仿真软件仿真，并进行实验验证，分析系统动态性能，得到响应曲线。结果表明，在拉纸速度65 mm/s下，跑偏量从1.5 mm降低到0.55 mm，该伺服系统位移和速度跟踪误差均较小。 |
| Abstract: |
| The servo correction control system was designed with RBF neural network through the establishment of its dynamic model. Using MATLAB / Simulink simulation software to simulate， and combining with experimental verification， the dynamic performance of the system was analyzed to obtain the response curve. The results showed that the deviation was reduced from 1.5 mm to 0.55 mm at the paper pulling speed of 65 mm/s， and the displacement and speed tracking error were small. |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
