TY - JOUR

T1 - A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architecture complexity

AU - Tang, Wai Sum

AU - Wang, Jun

N1 - Funding Information:
Manuscript received April 1, 1999; revised September 29, 1999. This work was supported by the Hong Kong Grants Council under Grant CUHK4165/98E. This paper was recommended by Associate Editor W. A. Gruver. The authors are with the Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, Shatin, NT, Hong Kong (e-mail: jwang@acae.cuhk.edu.hk). Publisher Item Identifier S 1083-4419(01)00376-9.

PY - 2001/2

Y1 - 2001/2

N2 - This paper presents an improved neural computation scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a previous neural network approach to minimum infinity-norm kinematic control, the present approach is less complex in terms of cost of architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four-degree-of-freedom planar robot arm and a seven-degree-of-freedom industrial robot are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators.

AB - This paper presents an improved neural computation scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a previous neural network approach to minimum infinity-norm kinematic control, the present approach is less complex in terms of cost of architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four-degree-of-freedom planar robot arm and a seven-degree-of-freedom industrial robot are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators.

KW - Infinity-norm joint velocity minimization

KW - Recurrent neural network

KW - Redundant manipulators

UR - http://www.scopus.com/inward/record.url?scp=0035246345&partnerID=8YFLogxK

U2 - 10.1109/3477.907567

DO - 10.1109/3477.907567

M3 - Article

AN - SCOPUS:0035246345

VL - 31

SP - 98

EP - 105

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 1

ER -