TY - GEN
T1 - Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network
AU - Liu, Shubao
AU - Wang, Jun
PY - 2005
Y1 - 2005
N2 - The bi-criteria joint torque optimization of kinematically redundant manipulators balances between the energy consumption and the torque distribution among the joints. In this paper, a simplified dual neural network is proposed to solve this problem. Joint torque limits are incorporated simultaneously into the proposed optimization scheme. The simplified dual network has less numbers of neurons compared with other recurrent neural networks and is proved to be globally convergent to optimal solutions. The control scheme based on the recurrent neural network is simulated with the PUMA 560 robot manipulator to demonstrate effectiveness.
AB - The bi-criteria joint torque optimization of kinematically redundant manipulators balances between the energy consumption and the torque distribution among the joints. In this paper, a simplified dual neural network is proposed to solve this problem. Joint torque limits are incorporated simultaneously into the proposed optimization scheme. The simplified dual network has less numbers of neurons compared with other recurrent neural networks and is proved to be globally convergent to optimal solutions. The control scheme based on the recurrent neural network is simulated with the PUMA 560 robot manipulator to demonstrate effectiveness.
UR - https://www.scopus.com/pages/publications/33750101901
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33750101901&origin=recordpage
U2 - 10.1109/IJCNN.2005.1556368
DO - 10.1109/IJCNN.2005.1556368
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780390482
SN - 9780780390485
VL - 5
SP - 2796
EP - 2801
BT - Proceedings of the International Joint Conference on Neural Networks
T2 - International Joint Conference on Neural Networks, IJCNN 2005
Y2 - 31 July 2005 through 4 August 2005
ER -