Efficient Control of Robot Manipulators with Model Disturbances

Abstract

The aim of this work is to present a new adaptive strategy for articulated robotic systems in order to reject adequately the tracking errors due to disturbances in the model. The technique is based on a well known robust controller for robotic manipulators proposed by Spong, which is derived using Lyapunov's direct method. The adaptation law acts in an essential parameter of the robust controller, the uncertainty bound parameter, in order to improve its performance. This paper presents real-time experimental results with a Puma-560 robot, comparing the performance of the new strategy with the classical PD controller with gravity compensation and with Spong's robust controller, in case of model uncertainties. It is shown that the adaptive controller improves the performance of previous control strategies