Two-layer recurrent neural network for kinematic control of redundant manipulators

Jun Wang, Qingni Hu, Dan chi Jiang

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

A recurrent neural network is presented for the kinematic control of kinematically redundant robot manipulators. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the signals of desired velocity of the end-effector are fed into the input layer, the output layer generates the joint velocity vector of the manipulator. The proposed recurrent neural network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Original languageEnglish
Pages (from-to)2507-2512
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume3
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: 10 Dec 199712 Dec 1997

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