A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control

Youshen S. Xia, Gang Feng, Jun Wang

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

Abstract

This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.

Original languageEnglish
Pages (from-to)54-64
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume35
Issue number1
DOIs
Publication statusPublished - Feb 2005
Externally publishedYes

Keywords

  • Constrained kinematic redundancy
  • Joint velocity
  • Joints limits
  • Primal-dual neural network

Fingerprint

Dive into the research topics of 'A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control'. Together they form a unique fingerprint.

Cite this