Open-Closed-Loop Iterative Learning Control With a Self-Adjustive Factor of the Gas Tungsten Arc Welding (GTAW) Process

Abstract

Human welder’s experiences and skills are critical for producing quality welds in manual gas tungsten arc welding (GTAW) process. For batch welding of the same workpiece, and the welding experience accumulated in same welding track, the welding process has a very high-degree reduplication. In this article, an open-closed-loop iterative learning control algorithm is constructed and implemented as an intelligent controller in automated GTAW process to reach the desired trajectory well. During the welding process, there will encounter external interference, such as random changes in voltage, resulting in pool surface fluctuations. Therefore, we introduce a self-adjustive factor based on ILC algorithm. The self-adjustive factor can adjust the input of the controller according to the error and error rate of change of the system, so that the system body has self-adaptation to improve the ability of anti-interference of the system. The simulation shows that a new proposed ILC control is an effective method for weld penetration in GTAW.

Keywords

• Gas tungsten arc welding
• self-adjustive factor
• open-closed-loop
• iterative learning control