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Super-twisting ZNN for coordinated motion control of multiple robot manipulators with external disturbances suppression

Dechao Chen, Shuai Li Orcid Logo, Qing Wu, Xin Luo

Neurocomputing

Swansea University Author: Shuai Li Orcid Logo

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Abstract

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 pra...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa52014
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Abstract: 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.
Keywords: Coordination motion control, Zeroing neural networks (ZNNs), Finite-time convergence, Super-twisting, Multiple robot manipulators
College: College of Engineering