An improved result of multiple model iterative learning control

2014 ◽  
Vol 1 (3) ◽  
pp. 315-322 ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xiaoli Li ◽  
Jian Liu ◽  
Linkun Wang ◽  
Kang Wang ◽  
Yang Li

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


2010 ◽  
Vol 73 (13-15) ◽  
pp. 2439-2445 ◽  
Author(s):  
Li Xiaoli ◽  
Zhang Wen

2014 ◽  
Vol 39 (9) ◽  
pp. 1564-1569 ◽  
Author(s):  
Xu-Hui BU ◽  
Fa-Shan YU ◽  
Zhong-Sheng HOU ◽  
Fu-Zhong WANG

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