Stochastic estimation of human arm impedance under nonlinear friction in robot joints: a model study

Abstract

The basic assumption of stochastic human arm impedance estimation methods is that the human arm and robot behave linearly for small perturbations. In the present work, the degree of influence of nonlinear friction in robot joints to the stochastic human arm impedance estimation is identified. Internal model based impedance control (IMBIC) is then proposed as a means of making the estimation accurate by compensating for the nonlinear friction. From simulations with a nonlinear Lugre friction model, it is observed that the reliability and accuracy of the estimation are severely degraded with nonlinear friction. In contrast, the combined use of stochastic estimation and IMBIC provides with accurate estimation results even with large friction. Furthermore, the performance of suggested method is independent of human arm and robot posture, and human arm impedance. Therefore, the IMBIC will be useful in measuring human arm impedance with conventional robot, as well as in designing a spatial impedance measuring robot, which requires gearing.