A fully neural-network-based planning scheme for torqueminimization of redundant manipulators
Han Ding
Tso, S.K.
Centre for Intelligent Design, Autom. & Manuf., City Univ. of Hong Kong, Kowloon;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb 1999
Volume: 46,
Issue: 1
On page(s): 199-206
ISSN: 0278-0046
References Cited: 16
CODEN: ITIED6
INSPEC Accession Number: 6169960
Digital Object Identifier: 10.1109/41.744412
Current Version Published: 2002-08-06
Abstract
The aim of this paper is to develop a new method for minimizing
joint torques of redundant manipulators in the Chebyshev sense and to
present a fully neural-network-based computational scheme for its
implementation. Minimax techniques are used to determine the null space
acceleration vector which can guarantee to minimize the maximum joint
torque. For real-time implementation, we transform the proposed method
into a computation of a recurrent neural network. At each time step, the
neural network is adopted for both the solution of the least-norm joint
acceleration and the determination of the optimum null space
acceleration vector. Compared with previous torque minimization schemes,
the proposed method enables more direct monitoring and control of the
magnitudes of the individual joint torques than does the minimization of
the sum of squares of the components. Simulation results demonstrate
that the proposed method is effective for the torque minimization
control of redundant manipulators
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