A comparison of control strategies of robotic manipulators using neural networks

Author

Gehlot, Narpat S. ; Alsina, Pablo J.

Author_Institution

Dept. de Engenharia Eletrica, Univ. Federal da Paraiba, Campina Grande, Brazil

fYear

1992

fDate

9-13 Nov 1992

Firstpage

688

Abstract

The authors present a comparison of control strategies for robotic manipulators based on artificial neural networks. Two position control strategies of a two-degree-of-freedom (DOF) SCARA manipulator are investigated: the direct inverse neurocontroller and the nonlinear neural compensator. The performances of the two strategies are compared in terms of error convergence and adaptation to parameter variation. Satisfactory simulation results of position control for the SCARA manipulator by using neurocontrollers are presented

Keywords

backpropagation; compensation; learning (artificial intelligence); manipulators; neural nets; position control; SCARA manipulator; control strategies; direct inverse neurocontroller; error backpropagation algorithm; error convergence; neural networks; nonlinear neural compensator; parameter variation adaptation; position control; robotic manipulators; training scheme; two-degree-of-freedom; Acceleration; Artificial neural networks; Manipulator dynamics; Motion control; Neural networks; Neurocontrollers; Parallel processing; Position control; Robot control; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control., Proceedings of the 1992 International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0582-5

Type

conf

DOI

10.1109/IECON.1992.254549

Filename

254549