Open AccessRocket Tank Surface Glue Layer Thickness Measurement Robot Trajectory Tracking Control Based on Adaptive Iterative Learning
Author(s) -
Hongze Xu,
Liu Li,
Yongjun Ni,
Siqi Liu,
Xiaodong Liu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/3/033076
Subject(s) - iterative learning control , trajectory , control theory (sociology) , robot , controller (irrigation) , rocket (weapon) , tracking (education) , process (computing) , computer science , tracking error , control engineering , simulation , engineering , artificial intelligence , control (management) , aerospace engineering , physics , psychology , pedagogy , astronomy , agronomy , biology , operating system
Thickness measurement of surface glue layer of rocket tank is a critical part to ensure the quality of rocket tank. When measuring, the industrial robot equipped with a non-contact thickness measurement end actuator performs measurement tasks along the planned trajectory. Because traditional position tracking algorithm inevitably has hysteresis, the actual trajectory of the control point has a large error with the ideal trajectory, which directly affects the thickness measurement accuracy. Iterative learning controller is used in this paper for trajectory tracking control of rocket tank surface glue thickness measurement robot. Considering the non-repetitive disturbance and time-varying factors in the measurement process, an adaptive iterative learning controller is proposed to improve the trajectory tracking control process of the thickness measurement robot. The effectiveness of the control method is verified by Matlab/Simulink simulation.