Virtual Experimental Analysis of Redundant Robot Manipulators Using Neural Networks

This study presents a theoretical–experimental scheme to control a redundant robot manipulator in the presence of unmodeled dynamics and discontinuous friction. The proposed control scheme does not require a priori knowledge of upper bounds, robot’s parameters, and external disturbance. The advantage of a feed-forward neural network (FFNN) controller is its robustness and ability to handle the model uncertainties. The virtual experimental results are carried out for a three-link planar redundant manipulator to show the effectiveness of the controller.

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