Comparative Analysis of DIDIM and IV Approaches using Double Least Squares Method

Abstract

Usually, identifying dynamic parameters for robots involves utilizing the Inverse Dynamic Model (IDM) which is linear in relation to the parameters being identified, alongside Linear Least Squares (LLS) methods. To implement this approach, precise measurements of both torque and position must be obtained at a high frequency. Additionally, velocities and accelerations must be estimated by implementing a band-pass filtering technique on the position data. Given the presence of noise in the observation matrix and the closed-loop nature of the identification process, we have modified the Instrumental Variable (IV) method to address the issue of noisy observations. A novel identification technique, named (Direct and Inverse Dynamic Identification Model) DIDIM, which requires only torque measurements as input variables, has recently been successfully applied to a 6-degree-of-freedom industrial robot. DIDIM employs a closed-loop output error approach that utilizes closed-loop simulations of the robot. The experimental results reveal that the IV and DIDIM methods exhibit numerical equivalence. In this paper, we conduct a comparison of these two methods using a double step least squares (2SLS) analysis. We experimentally validate this study using a 2-degree-of-freedom planar robot.