基于T-S模型和模糊神经网络的焊接电源群控

Group control system of welding power supplies based on T-S model and ANFIS

关键词:  模糊神经推理  焊接电源  群控  T-S模型  仿真

Key words:  adaptive neuro-fuzzy inference system  welding power source  group-control  Tagaki-Sugeno model  simulation

摘要:

利用多台焊接电源同时对同一工件进行焊接,当外电压波动时,众焊接电源依靠自身控制系统进行各自调节的过程也是对外电网干扰的再生过程。将模糊理论与神经网络相结合,并应用于多焊接电源的群控。在分析和设计了状态变量的隶属度函数、推理规则、解模糊算法等基础上,完成了基于T-S(Tagaki-Sugeno)模型的自适应模糊神经推理控制器设计。利用该控制模型在Simulink搭建的焊接电源群控模型上进行仿真。结果表明,该控制模型具有调整时间短,超调量小的优点(与众焊接电源各自单独调节相比较,调整时间缩短了22%,超调量减小了40%),反映出良好的动态特性。

Abstract:

The adjusting process by the welding power which depends on its own control system is also the reborn process of the outer power net interference, if the outer-voltage fluctuates in the system when more than one welding power were used to weld the same workpiece simultaneously.The group-control sy stem of welding power sources with the combination of fuzzy theory and neural networkswas was studied firstly.The membership function of state variables, inference rules in the system, algorithms for fuzzy inference and defuzzification, etc.were analyzed and devised subsequently. Based on those, adaptive neuro-fuzzy inference system (ANFIS) with Tagaki-Sugeno model was achieved.Finally the completed ANFIS was simulated by using Simulink toolbox in Matlab, and used in Membrane-water-wall welding machine for experimentation.The result shows that the design is superior at less regulating time (shortened by 22%, compared with each welding power adjust separately) and less overshoot (reduced by 40%), which reflects its excellent dynamic behavior.