Recurrent-neural-network-based velocity-level redundancy resolution for manipulators subject to a joint acceleration limit

Yinyan Zhang, Shuai Li, Xuefeng Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

For the safe operation of redundant manipulators, physical constraints such as the joint angle, joint velocity, and joint acceleration limits should be taken into account when designing redundancy resolution schemes. Velocity-level redundancy resolution schemes are widely adopted in the kinematic control of redundant manipulators due to the existence of the well-tuned inner loop regarding the joint velocity control. However, it is difficult to deal with joint acceleration limits for velocity-level redundancy resolution methods. In this paper, a recurrent-neural-network-based velocity-level redundancy resolution method is proposed to deal with the problem, and theoretical results are given to guarantee its performance. By the proposed method, the end-effector position error is asymptotically convergent to zero, and all the joint limits are not violated. The effectiveness and superiority of the proposed scheme are validated via simulation results.

Original languageEnglish
Article number8408695
Pages (from-to)3573-3582
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number5
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Joint limits
  • kinematic control
  • manipulator
  • neural network
  • redundancy resolution

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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