Open AccessStability Control of Transport Robot Based on Iterative Learning Control
Author(s) -
Tingrui Liu,
Yan Ding,
Pan Wang,
Kang Zhao,
Jiahao Jia
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2173/1/012061
Subject(s) - iterative learning control , control theory (sociology) , tracking (education) , trajectory , tracking error , torque , stability (learning theory) , process (computing) , position (finance) , computer science , interference (communication) , motion control , robot , control engineering , engineering , control (management) , artificial intelligence , physics , psychology , computer network , pedagogy , channel (broadcasting) , finance , astronomy , machine learning , economics , thermodynamics , operating system
In this study, stability control for transport process of transport robot subjected to 2R manipulator movement, is investigated based on iterative learning control (ILC). The joint positions, speeds and accelerations are used as variables to establish the expression of driving torques of manipulator joints. According to the experience, the linear interference torque in the process of motion is determined. Three ILC algorithms are applied to achieve stability control, and good trajectory tracking results are obtained. Position tracking, speed tracking, and the maximum position error in the process of tracking are illustrated, and through the absolute value of the maximum error, the optimal iterative algorithm is finally determined.