基于神经网络 PID 的造纸机多电机同步控制研究

目的为提高生产的质量和降低纸的断头率,研究造纸机多电机同步控制的方法。方法应用Matlab/Simulink建立模型和仿真实验,分析主从同步控制结构和并行同步控制结构,在并行同步控制结构的基础上,引入基于神经网络PID算法的速度补偿器。结果仿真结果表明,引入速度补偿后,2台电机的转速逐渐上升至给定速度,且速比稳定保持在1.01。在t=2 s只有第2台电机的负载转矩发生变化时,2台电机的转速基本没有下降。结论该方法增强了造纸机多电机同步控制系统的同步跟随能力,提升了抗干扰性能,可以有效避免在造纸机运行过程中,电机负载转矩突变造成的纸张过紧断纸、过松褶皱,为造纸机多电机提供了一种新的同步控制的方法。

Multi-motor Synchronous Control of Paper Machine Based on Neural Network PID

A multi-motor synchronous control strategy of paper machine was applied to improve the production quality and reduce the breakage rate of paper. The neural network PID algorithm based on parallel synchronization was determined by comparing with the structure of master-slave synchronization control and parallel synchronization control, for which modeling and simulating were conducted by Matlab/Simulink. The results showed that after the introduction of speed compensation, the speed of the two motors gradually increased to a given speed and maintained a stable ratio 1.01. At t=2 s, only the load torque of the second motor was changed, but the rotational speed of the two motors substantially did not decrease. This method enhances the synchronization ability of multi-motor synchronous control system and improves the anti-jamming performance. It can effectively prevent the motor load torque caused by mutations in tight paper breaking or loose folds in the process of paper machine operation, and provide a new method for synchronous control.