Stable computed-torque control of robot manipulators via fuzzy self-tuning

Abstract

Computed-torque control is a well-known motion control strategy for manipulators which ensures global asymptotic stability for fixed symmetric positive definite (proportional and derivative) gain matrices. In this paper, we show that global asymptotic stability also holds for a class of gain matrices depending on the manipulator state. This feature increases the potential of the computed-torque control scheme to handle practical constraint in actual robots such as presence of friction in the joints and actuators with limited torque capabilities. We illustrate this potential by means of a fuzzy self-tuning algorithm to select the proportional and derivative gains according to the actual tracking position error. Experiments on a two degrees of freedom robot arm show the usefulness of the proposed approach.