A dual neural network for kinematic control of redundant robot manipulators

Y. Xia, J. Wang

Research output: Contribution to journalLetterpeer-review

191 Citations (Scopus)

Abstract

The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is proven to be globally exponentially stable. The proposed dual network is also shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Original languageEnglish
Pages (from-to)147-154
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume31
Issue number1
DOIs
Publication statusPublished - Feb 2001
Externally publishedYes

Keywords

  • Inverse kinematics
  • Kinematically redundant manipulators
  • Recurrent neural networks

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