Real-time control of industrial manipulator vibration using artificial neural networks

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This data was imported from Scopus:

Authors: Marcham, L.J., Rao, B.K.N., Noroozi, S. and Penson, R.P.

Journal: IEEE International Conference on Neural Networks - Conference Proceedings

Volume: 5

Pages: 2550-2554

The work reported in this paper addresses the control of robot manipulator vibration, with the specific aim of achieving a greater degree of dynamic accuracy. An overview of existing work on the modelling of robot dynamics and neural control is reported. A model of the dynamics of a two degrees of freedom manipulator, inclusive of vibration, is presented and is used to train a time-delay neural network to learn the predicted end-vector vibration. The results are compared with experimental data taken from a PUMA562C industrial manipulator using laser interferometry. Control of an end-effector located, active compensator, based upon on-line training of an artificial neural network controller is discussed and recommendations which form the basis of further investigations, currently being undertaken, are presented.

This data was imported from Web of Science (Lite):

Authors: Marcham, L.J., Rao, B.K.N., Noroozi, S., Penson, R.P. and IEEE

Journal: 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6

Pages: 2550-2554

The data on this page was last updated at 05:17 on May 25, 2020.