基于RBF神经网络的纸张定量水分解耦控制系统设计

Paper Basis Weight and Moisture Decoupled Control Based on RBF Neural Network

中文关键词:  RBF神经网络  定量  水分  解耦控制

Key Words:RBF neural network  basis weight  moisture  decoupling control

中文摘要:

      抄纸过程中纸机系统具有大滞后、非线性、时变等特点，纸张定量与水分之间存在强耦合效应，针对这些问题，设计了一种基于RBF神经网络的PID解耦控制方法。利用RBF神经网络辨识定量与水分的数学模型，实时调整PID控制器的参数，实现系统的解耦功能。仿真结果表明，该方法具有良好的静态、动态性能和很强的自适应性，能有效解决纸张定量和水分之间的耦合作用。

Abstract:

      There are the characteristics such as high hysteresis, non-linear and time varying in papermaking process, and strong couplings are formed between basis weight and moisture content of the paper. In order to solve the coupling problem, a PID decoupling controller based on the identifier of RBF neural network is proposed. This controller can identify the system model and self-adjust the PID parameters by using RBF network to realize the decoupling control .The simulation results prove that the controller can improve the dynamic and static characteristics of the control system, and is suited to paper basis weight and moisture decoupling control.