Experimental studies of neural network impedance force control for robot manipulators

Abstract

In this paper, the neural network force control is presented. Under the framework of impedance control, neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment. A modified simple impedance function is realized after the convergence of the neural network. Learning algorithms for the neural network to minimize the force error directly are designed. As a test-bed, the large X-Y table robot was implemented. Experimental results obtained show better force tracking when the neural network is used.