On neural network application to robust impedance control of robot manipulators

Abstract

Performance of impedance controller for robot force tracking is affected by the uncertainties in the robot model and environment stiffness. The purpose of the paper is to improve the controller robustness by applying the neural network technique to compensate for the uncertainties in the robot model. A novel error signal is proposed for the neural network training. In addition, an algorithm is developed to determine the reference trajectory when the environment stiffness is unknown. Simulations show that highly robust position/force tracking by a three degrees-of-freedom robot can be achieved under large uncertainties.