A novel recurrent neural network for manipulator control with improved noise tolerance

Shuai Li, Huanqing Wang, Muhammad Usman Rafique

Research output: Journal article publicationJournal articleAcademic researchpeer-review

104 Citations (Scopus)


In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the high-order derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations.

Original languageEnglish
Pages (from-to)1908-1918
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Publication statusPublished - 1 May 2018


  • Kinematic control
  • Noise
  • Recurrent neural network
  • Redundant manipulator

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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