Hysteresis identification of joint with harmonic drive transmission based on Monte Carlo method

In this study, we analyze the hysteresis behavior of robotic joints equipped with harmonic drivers under various speeds and loads, focusing on a range of hysteresis models including the Bouc–Wen (BW), Fractional Order Bouc–Wen (FOBW), and their integration with the Krasnoselskii–Pokrovskii (KP) model. This integration, which forms the KP-FOBW model, is a novel aspect of our research, offering an enhanced understanding of non-linear and hysteresis behaviors in robotic joints. We employ the Monte Carlo method, particularly focusing on the Stochastic Gradient Hamiltonian Monte Carlo (SGHMC), for precise parameter identification. Our results demonstrate that the FOBW model, especially when combined with the KP model, provides a more accurate representation of the hysteresis curves under varying operating conditions. The KP-FOBW combination emerges as a powerful tool to predict the output torque in robotic joints. This study contributes to a deeper understanding of hysteresis in joints with harmonic drivers, showcasing its potential for complex dynamic modeling in robotics.