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On-line Neural Network Compensator for Constrained Robot Manipulators Shenghai Hu, Marcel0 H. Ang Jr., and H. Krishnan
 

Summary: On-line Neural Network Compensator for Constrained Robot Manipulators
Shenghai Hu, Marcel0 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 new neural network controller for the
constrained robot manipulators in task space is
presented. The neural network will be used for adaptive
compensation of the structured and unstructured
uncertainties. The controller consisted of a model-based
term and a neural network on-line adaptive compensation
term. It is shown that the neural network adaptive
compensation is universally able to cope with totally
different classes of system uncertainties. Novel adaptive
learning algorithms for tuning the weights of neural
network are proposed. A suitable error filtered signal for
training the neural network can be easily obtained from
the controller design without using any model knowledge

  

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

 

Collections: Engineering