A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints

Wai Sum Tang; Jun Wang

doi:10.1109/ICSMC.1999.812504

A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints

Wai Sum Tang, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents the manipulator from torque saturation and hence ensuring a good tracking accuracy. The neural network is proven to be globally convergent to the optimal solution. The simulation results show that the neural network is capable of effectively computing the optimal redundancy resolution. © 1999 IEEE

Original language	English
Title of host publication	IEEE SMC'99 Conference Proceedings
Publisher	IEEE
Pages	782-787
Volume	4
ISBN (Print)	0-7803-5731-0
DOIs	https://doi.org/10.1109/ICSMC.1999.812504
Publication status	Published - Oct 1999
Externally published	Yes
Event	1999 IEEE International Conference on Systems, Man, and Cybernetics: 'Human Communication and Cybernetics' - Tokyo, Japan Duration: 12 Oct 1999 → 15 Oct 1999

Publication series

Name	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X
ISSN (Electronic)	0884-3627

Conference

Conference	1999 IEEE International Conference on Systems, Man, and Cybernetics
Country/Territory	Japan
City	Tokyo
Period	12/10/99 → 15/10/99

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10.1109/ICSMC.1999.812504

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@inproceedings{1923a169d05241d1993d23d27de39667,

title = "A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints",

abstract = "In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents the manipulator from torque saturation and hence ensuring a good tracking accuracy. The neural network is proven to be globally convergent to the optimal solution. The simulation results show that the neural network is capable of effectively computing the optimal redundancy resolution. {\textcopyright} 1999 IEEE",

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

year = "1999",

month = oct,

doi = "10.1109/ICSMC.1999.812504",

language = "English",

isbn = "0-7803-5731-0",

volume = "4",

series = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",

publisher = "IEEE",

pages = "782--787",

booktitle = "IEEE SMC'99 Conference Proceedings",

address = "United States",

note = "1999 IEEE International Conference on Systems, Man, and Cybernetics : 'Human Communication and Cybernetics' ; Conference date: 12-10-1999 Through 15-10-1999",

}

Tang, WS & Wang, J 1999, A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints. in IEEE SMC'99 Conference Proceedings. vol. 4, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp. 782-787, 1999 IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan, 12/10/99. https://doi.org/10.1109/ICSMC.1999.812504

A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints. / Tang, Wai Sum; Wang, Jun.
IEEE SMC'99 Conference Proceedings. Vol. 4 IEEE, 1999. p. 782-787 (Proceedings of the IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints

AU - Tang, Wai Sum

AU - Wang, Jun

PY - 1999/10

Y1 - 1999/10

N2 - In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents the manipulator from torque saturation and hence ensuring a good tracking accuracy. The neural network is proven to be globally convergent to the optimal solution. The simulation results show that the neural network is capable of effectively computing the optimal redundancy resolution. © 1999 IEEE

AB - In this paper, a primal-dual neural network is proposed for the joint torque optimization of redundant manipulators subject to torque limit constraints. The neural network generates the minimum driving joint torques which never exceed the hardware limits and keep the end-effector to track a desired trajectory. The consideration of physical limits prevents the manipulator from torque saturation and hence ensuring a good tracking accuracy. The neural network is proven to be globally convergent to the optimal solution. The simulation results show that the neural network is capable of effectively computing the optimal redundancy resolution. © 1999 IEEE

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DO - 10.1109/ICSMC.1999.812504

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0-7803-5731-0

VL - 4

T3 - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

SP - 782

EP - 787

BT - IEEE SMC'99 Conference Proceedings

PB - IEEE

T2 - 1999 IEEE International Conference on Systems, Man, and Cybernetics

Y2 - 12 October 1999 through 15 October 1999

ER -

A Primal-dual neural network for joint torque optimization of redundant manipulators subject to torque limit constraints

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