Adaptive characterisation of a human hand model during intercations with a telemanipulation system

Abstract

Proper modeling of the human arm dynamic, as it interacts with telemanipulation and haptic systems, is important in enhancing the transparency of these systems. In this article, we introduced an adaptive identifier to estimate the impedance characteristic of a human operator as it interacts with a single translational degree of freedom mechanism. The five parameter model, including an extra spring and damper for a better approximation of the dynamic behavior of human arm, has been used. Since the impedance characteristic of human arm differs from one individual to another, it is important to estimate these parameters for each individual and update the controller to enhance the transparency and stability of the system. An adaptive architecture with normalized least square method with forgetting factor has been used for on-line identification of these five unknown impedance parameters of the human arm. The results illustrates that the estimated parameters properly converge to actual parameters if the richness condition (richness on input data) is satisfied. It is shown that the suggested adaptive architecture has the ability to identify the impedance characteristics of different operators.