A Lagrangian network for kinematic control of redundant robot manipulators

Jun Wang, Qingni Hu, Danchi Jiang

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)

Abstract

A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Original languageEnglish
Pages (from-to)1123-1132
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume10
Issue number5
DOIs
Publication statusPublished - 1999
Externally publishedYes

Keywords

  • Asymptotic stability
  • Kinematic control
  • Kinematically redundant manipulators
  • Optimization method
  • Recurrent neural networks

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