Efficient Control of Robot Manipulators with Model Disturbances

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