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Neural Network Controller for Constrained Robot Manipulators Shenghai Hu, Marcelo H. Ang Jr., and H. Krishnan
 

Summary: 1
Neural Network Controller for Constrained Robot Manipulators
Shenghai Hu, Marcelo H. Ang Jr., and H. Krishnan
Dept of Mechanical and Production Engineering
National University of Singapore
Singapore 119260
mpeangh@nus.edu.sg
Abstract
In this paper, a neural network controller for
constrained robot manipulators is presented. A feed-
forward neural network is used to adaptively compensate
for the uncertainties in the robot dynamics. Training
signals are proposed for the feed-forward neural network
controller. The neural network weights are tuned on-line,
with no off-line learning phase required. It is shown that
the controller is able to deal with the uncertainties of the
robot, which include modelled undertainties (dynamic
parameter uncertainties, etc.) as well as unmodelled
uncertainties (frictions, etc). The suggested controller is
simple in structure and can be implemented easily. The

  

Source: Ang Jr.,, Marcelo H. - Department of Mechanical Engineering, National University of Singapore

 

Collections: Engineering