Object property identification using uncertain robot manipulator

Abstract

This paper presents a learning control algorithm to identify object properties by an uncertain robot manipulator. On one hand, for the robot system with unknown (or immeasurable) parameters, the manipulator dynamics properties are uncertain so we use adaptive parameter learning law to estimate the practically unknown dynamics. On the other hand, a reference model is specified to be followed. In order to identify the geometry and elasticity of the interacting object, the reference point and feedforward force in reference model is adapted in each trial. Because the updating of the reference model utilizes the estimated parameters, the learning law of parameter estimation is thus designed to guarantee the convergence of parameter estimation in finite time (FT). Simulation studies demonstrate the effectiveness of our proposed method.