Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints

Yinyan Zhang, Shuai Li, Seifedine Kadry, Bolin Liao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)


Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the proposed controller.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusAccepted/In press - 10 Aug 2018


  • Disturbance
  • kinematic control
  • Kinematics
  • Manipulators
  • neural network
  • Optimization
  • optimization
  • Performance analysis
  • Recurrent neural networks
  • redundant manipulator
  • Task analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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