Neural network controller for constrained robot manipulators

Abstract

In this paper, a neural network controller for constrained robot manipulators is presented. A feed-forward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the feed-forward neural network controller. The neural network weights are tuned on-line, with no off-line learning phase required. It is shown that the controller is able to deal with the uncertainties of the robot, which include modelled uncertainties (dynamic parameter uncertainties, etc.) as well as unmodelled uncertainties (frictions, etc). The suggested controller is simple in structure and can be implemented easily. The controller has the Proportional-Integral (PI) type force feedback control structure with a low proportional force feedback gain. Detailed experimental results show the effectiveness of the proposed controller.