基于模糊神经网络PID的卷材纠偏控制

目的为了提高卷材边缘的整齐度,提升包装产品质量,降低包装材料损耗。方法分析卷材放卷过程中跑偏的原因,在分析放卷纠偏系统数学模型的基础上,提出一种模糊神经网络PID的纠偏控制器以实现复杂的卷材放卷系统参数自调整以及优化。结果仿真和实验结果表明,模糊神经网络PID具有更快的响应速度,超调量更小,纠偏控制精度达到±0.5 mm。结论所述控制方法能够明显降低软性包装材料跑偏误差,大大提高了产品包装质量。

Coil Deviation Control Based on Fuzzy Neural Network PID

The work aims to improve the coil edge uniformity, improve the quality of the packaging products and reduce the packaging material loss. The cause for coil deviation in the unwinding process was analyzed. Based on the analysis of the mathematical model for the unwinding deviation control system, a deviation controller based on the fuzzy neural network PID was proposed to achieve the complicated parameter self adjustment and optimization of the coil unwinding system. The simulation and experimental results showed that, the fuzzy neural network PID had faster response speed and smaller overshoot, and the deviation control accuracy was up to ±0.5 mm. The proposed control method can obviously reduce the deviation error of the flexible packaging material, and greatly improve the packaging quality of the product.