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

Dechao Chen, Shuai Li, Faa Jeng Lin, Qing Wu

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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.

Original languageEnglish
Article number8792375
Pages (from-to)2651-2660
Number of pages10
JournalIEEE Transactions on Cybernetics
Volume50
Issue number6
DOIs
StatePublished - Jun 2020

Keywords

  • Finite-Time convergence
  • robot manipulators
  • robustness
  • super-Twisting (ST)
  • tracking control
  • zeroing neural-dynamics (ZNDs)

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