Title :
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
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;
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
DOI :
10.1109/IECON.1992.254549
