A dual neural network for constrained joint torque optimization of kinematically redundant manipulators

Yunong Zhang, Jun Wang

Research output: Contribution to journalLetterpeer-review

87 Citations (Scopus)

Abstract

A dual neural network is presented for the real-time joint torque optimization of kinematically redundant manipulators, which corresponds to global kinetic energy minimization of robot mechanisms. Compared to other computational strategies on inverse kinematics, the dual network is developed at the acceleration level to resolve redundancy of limited-joint-range manipulators. The dual network has a simple architecture with only one layer of neurons and is proved to be globally exponentially convergent to optimal solutions. The dual neural network is simulated with the PUMA 560 robot arm to demonstrate effectiveness.

Original languageEnglish
Pages (from-to)654-662
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume32
Issue number5
DOIs
Publication statusPublished - Oct 2002
Externally publishedYes

Keywords

  • Dual neural network
  • Joint limits
  • Joint torque optimization
  • Redundant manipulators

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