Identifying Current Dynamics of Robot Payload Based on Iterative Weighting Estimation

Accurate payload dynamics benefit robotic applications, such as model-based control. However, existing methods rely on joint torque signals for identifying payload dynamics. For these robots that are not equipped with joint torque sensors, the torques can be determined merely via the multiplication of actuator currents and joint torque constants. Unfortunately, the uncertainty and identification errors of joint torque constants can lead to deviations in joint torque estimation, which, in turn, increases the cumulative identification errors for payload dynamics. To tackle this problem, a new approach to identification for payload dynamics employing joint currents rather than joint torques is developed in this article. Unlike traditional methods that use estimated joint torques, the proposed method only uses measured joint currents. It avoids the identification errors of joint torque constants and the estimation errors of joint torques, thereby reducing the cumulative identification errors in payload dynamics. To the best of the authors’ knowledge, it is an initial effort to identify the payload’s current dynamics. This article has four main contributions. The first one is to develop a novel model for payload identification according to current dynamics. The second one is to propose an identification method for estimating the payload’s current dynamics based on iterative weighting. The third one is to integrate a zero-velocity continuous nonlinear friction model and apply it to the identification of current dynamics. Ultimately, experimental evaluations are performed to compare the proposed approach with four other methods, and the results demonstrate the advantages of the proposed approach.