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New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust Solution

Dechao Chen, Shuai Li Orcid Logo, Faa-Jeng Lin, Qing Wu

IEEE Transactions on Cybernetics, Pages: 1 - 10

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/TCYB.2019.2930662

Abstract

Parallel robots are usually required to perform real-time tracking control tasks in the presence of external disturbances in the complex environment. Conventional zeroing neural-dynamics (ZNDs) provide an alternative solution for the real-time tracking control of parallel robots due to its capacity...

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Published in: IEEE Transactions on Cybernetics
ISSN: 2168-2267 2168-2275
Published: 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa52001
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Abstract: Parallel robots are usually required to perform real-time tracking control tasks in the presence of external disturbances in the complex environment. Conventional zeroing neural-dynamics (ZNDs) provide an alternative solution for the real-time tracking control of parallel robots due to its capacity of parallel processing and nonlinearity handling. However, it is still a challenge for the solution in a unified framework of the ZND to deal with the external disturbances, and simultaneously possess a finite-time convergence property. In this paper, a novel ZND model by exploring the super-twisting (ST) algorithm, named ST-ZND model, is proposed. The theoretical analyses on the global stability, finite-time convergence, as well as the robustness against the external disturbances are rigorously presented. Finally, the effectiveness and superiority of the ST-ZND model for the real-time tracking control of parallel robots are demonstrated by two illustrative examples, comparisons, and convergence tests.
Start Page: 1
End Page: 10

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