Infinity-norm torque minimization for redundant manipulators using a recurrent neural network

Wai Sum Tang; Jun Wang; Yangsheng Xu

doi:10.1109/CDC.1999.831241

Infinity-norm torque minimization for redundant manipulators using a recurrent neural network

Wai Sum Tang
, Jun Wang
, Yangsheng Xu

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

1 Citation (Scopus)

Abstract

A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its input, and the minimum infinity-norm joint torques is generated at the same time as its output. It is shown that the recurrent neural network is capable of effectively generating the minimum infinity-norm joint torque redundancy resolution of manipulators.

Original language	English
Pages (from-to)	2168-2173
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	3
DOIs	https://doi.org/10.1109/CDC.1999.831241
Publication status	Published - 1999
Externally published	Yes
Event	The 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA Duration: 7 Dec 1999 → 10 Dec 1999

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title = "Infinity-norm torque minimization for redundant manipulators using a recurrent neural network",

abstract = "A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its input, and the minimum infinity-norm joint torques is generated at the same time as its output. It is shown that the recurrent neural network is capable of effectively generating the minimum infinity-norm joint torque redundancy resolution of manipulators.",

author = "Tang, \{Wai Sum\} and Jun Wang and Yangsheng Xu",

year = "1999",

doi = "10.1109/CDC.1999.831241",

language = "English",

volume = "3",

pages = "2168--2173",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0743-1546",

publisher = "IEEE",

note = "The 38th IEEE Conference on Decision and Control (CDC) ; Conference date: 07-12-1999 Through 10-12-1999",

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TY - JOUR

T1 - Infinity-norm torque minimization for redundant manipulators using a recurrent neural network

AU - Tang, Wai Sum

AU - Wang, Jun

AU - Xu, Yangsheng

PY - 1999

Y1 - 1999

N2 - A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its input, and the minimum infinity-norm joint torques is generated at the same time as its output. It is shown that the recurrent neural network is capable of effectively generating the minimum infinity-norm joint torque redundancy resolution of manipulators.

AB - A recurrent neural network is applied for minimizing the infinity-norm of joint torques in redundant manipulators. The recurrent neural network explicitly minimizes the maximum component of joint torques in magnitude while keeping the relation between the joint torque and the end-effector acceleration satisfied. The end-effector accelerations are given to the recurrent neural network as its input, and the minimum infinity-norm joint torques is generated at the same time as its output. It is shown that the recurrent neural network is capable of effectively generating the minimum infinity-norm joint torque redundancy resolution of manipulators.

UR - https://www.scopus.com/pages/publications/0033307741

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0033307741&origin=recordpage

U2 - 10.1109/CDC.1999.831241

DO - 10.1109/CDC.1999.831241

M3 - RGC 22 - Publication in policy or professional journal

SN - 0743-1546

VL - 3

SP - 2168

EP - 2173

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - The 38th IEEE Conference on Decision and Control (CDC)

Y2 - 7 December 1999 through 10 December 1999

ER -

Infinity-norm torque minimization for redundant manipulators using a recurrent neural network

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