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

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

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.