The welding of the same parts has same welding trajectory, so welding process has strong repeatability. In this paper, aiming at\nthe repeatability of welding process, an iterative learning controller is designed to achieve the control of weld quality. Due to\nthe extremely variable welding environment and the presence of noise interferences and load disturbances, it is easy to cause the\njumping change in parameters and even the structure of the welding system.Therefore, the idea of multiplemodel adaptive control\n(MMAC) is introduced into iterative learning control (ILC), and amultiple model iterative learning control (MMILC) algorithm is\ndesigned according to model of weld pool dynamic process in gas tungsten arc welding (GTAW). Besides, the convergence of the\nalgorithm is analyzed for two cases: fixed parameters and jumping parameters. It turns out that the MMILC can not only utilize\nthe repetitive information effectively in the welding process to achieve high precision tracking control of weld seam in limited time\ninterval, but also realize the multiple model switching according to different working conditions to improve the welding quality.
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