基于动态模糊神经网络的铝合金脉冲MIG焊接过程解耦控制分析
Decoupling control analysis of aluminum alloy pulse MIG welding process based on dynamic fuzzy neural networks
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摘要: 针对铝合金脉冲MIG焊接过程参数间强耦合、不稳定等关键问题,在介绍DFNN结构、学习算法基础上,设计了基于D-FNN的解耦控制器,并对同步、非同步与加干扰脉冲输入信号下分别对以脉冲电流占空比、送丝速度为输入量,以焊丝伸出长度(干伸长)、焊缝正面熔宽为被控量的铝合金脉冲MIG焊多输入多输出(multiple-input multiple-output,MIMO)过程进行了动态解耦控制仿真研究,仿真结果表明,D-FNN控制器能够实时进化规则,动态调整学习因子,实现了MIMO过程的完全解耦,满足焊接实时控制要求且具有快的响应速度、较好的鲁棒性,为铝合金脉冲MIG焊MIMO过程稳定焊接提供了一种新型的实时解耦控制方法.Abstract: Considering the strong coupling of parameters, unstable and other key issues during aluminum alloy pulse MIG welding process,D-FNN structure and learning algorithm were introduced.The decoupling controllers were designed based on D-FNN.The dynamic decoupling control simulation of aluminum alloy pulse MIG welding multiple-input multiple-output (MIMO) process,setting the duty cycle of pulse current and wire feeding speed as inputs but wire extension and weld width as outputs, was investigated with synchronization,asynchronous and adding interference pulse.The simulation results indicate that D-FNN controller could real-time evolve rules,dynamically adjust the learning factors,completely decouple the MIMO process and meet the real-time control requirements of welding process.In addition,its fast response speed and good robustness provided a new real-time decoupling control method for stabilizing the aluminum alloy pulsed MIG welding process.