Two-layer recurrent neural network for real-time control of redundant manipulators with torque minimization

W. S. Tang, J. Wang

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

A recurrent neural network for kinematic control of redundant robot manipulators with torque minimization is presented. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the command signals of desired acceleration of the end-effector are fed into the input layer, the output layer generates the joint acceleration vector of the manipulator with joint torques being minimized. The proposed recurrent neural network is shown to be capable of asymptotic tracking of trajectory for the redundant manipulators with minimized joint torques.

Original languageEnglish
Pages (from-to)1720-1724
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - 1998
Externally publishedYes
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
Duration: 11 Oct 199814 Oct 1998

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