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Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression
Chen, Dechao1,2; Li, Shuai3; Wu, Qing1; Luo, Xin4
2020-01-02
摘要This paper considers the coordination motion control of multiple robot manipulators by developing a unified framework of super-twisting zeroing neural network (ST-ZNN), and proposes a novel external disturbances suppression model. The proposed ST-ZNN model makes new progresses of both theory and practice by overcoming two limitations in the conventional ZNN (CZNN) models, i.e., the convergence time tending to be infinitely large and the rejection of external disturbances staying at the stage of asymptotic convergence. Then, the global stability, finite-time convergence, and robustness against external disturbances are rigorously proven in the theory. Finally, illustrative coordination motion control tasks, comparisons and performance tests demonstrate the effectiveness and superiority of the proposed ST-ZNN model for coordination motion control of multiple robot manipulators. (C) 2019 Published by Elsevier B.V.
关键词Coordination motion control Zeroing neural networks (ZNNs) Finite-time convergence Super-twisting Multiple robot manipulators
DOI10.1016/j.neucom.2019.08.085
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号371页码:78-90
通讯作者Li, Shuai(shuaili@polyu.edu.hk)
收录类别SCI
WOS记录号WOS:000493950600007
语种英语