首页> 外文会议>Conference of China University Society on Manufacturing Automation >Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply
【24h】

Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply

机译:倒等离子体弧切割电源模糊神经网络研究

获取原文

摘要

A variable interval fuzzy quantification algorithm with self-adjustable factor in full domain is proposed in this paper. It focuses on digital inverted plasma arc cutting power and studies strong nonlinearity and uncertainty of power. The neural network is also introduced to decouple cutting parameters variables in the multi-parameters coupling cutting process. This algorithm avoids complex nonlinear system modeling and realizes real-time and effective online control of cutting process by combining advantages of fuzzy control and neural network control. Furthermore, the optimized fuzzy control improves steady-state precision and dynamic performance of system simultaneously. The experimental result shows that this control improves precision, ripples, finish and other comprehensive index of work piece cut, and plasma arc cutting power supply based on fuzzy-neural network has excellent control performance.
机译:本文提出了一种可变间隔模糊量化算法,具有全域的自调节因子。它侧重于数字倒等离子体弧形切割力,研究强大的非线性和功率的不确定性。在多参数耦合切割过程中还引入了神经网络以使切割参数变量分离。该算法避免了复杂的非线性系统建模,并通过组合模糊控制和神经网络控制的优点来实现对切割过程的实时和有效的在线控制。此外,优化的模糊控制同时提高了系统的稳态精度和动态性能。实验结果表明,该控制提高了精度,涟漪,饰面等工件切割综合指标,基于模糊神经网络的等离子电弧切削电源具有优异的控制性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号