An Approach for Identifying Dynamic Parameters in Robotic Systems With Inconsistent Joint Measurements

Abstract

Achieving accurate robot control and realistic robot simulation relies on the precise modeling of robotic dynamics. Although the identification method for obtaining dynamic parameters has been developed for several years, joint inconsistency has largely been disregarded in prior studies. The inconsistency of joint actuators results in varying confidence levels of their measurement results, leading to the departure of the final identification parameters from valid values, thereby impacting the control performance. This paper presents a novel identification method that effectively addresses the issue of joint inconsistency by assigning distinct weights to each joint. The presented approach extends the least square (LS) method and incorporates the particle swarm optimization (PSO) algorithm to calculate the weights of each individual joint. This approach is referred to as PSO-based weighted least square (PWLS). Simulation experiments demonstrated the superior identification accuracy of the PWLS method compared to the LS method in a robot system characterized by joint inconsistency. Moreover, the experiments were performed on a three degrees-of-freedom (DOFs) robotic limb, which exhibited improved identification performance in both the excitation and verification trajectories. These findings have promising implications for enhancing the control and simulation of robotic systems.