Parameter Identification of Heavy-Duty Manipulator Using Stochastic Gradient Hamilton Monte Carlo Method

Abstract

This study focuses on the parameter identification of a heavy-duty manipulator used in the remote maintenance of the China Fusion Engineering Test Reactor (CFETR). Accurate modeling of the manipulator's dynamics needs investigation of the effects of hysteresis, velocity, and other variables on output torque independently. For this reason, we estimate the undetermined model parameters using the Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) method. In addition, a global sensitivity analysis is performed to assess the precision of the model's output and the relative significance of its input variables. Experiments are conducted to determine the output torque, hysteresis displacement and velocity of the CFETR's heavy-duty manipulator. Our findings indicate that the SGHMC method significantly improves the efficacy of parameter identification while maintaining a high level of accuracy, resulting in a significant reduction of approximately 8% in the root mean square error (RMSE) of the output torque. In addition, the analysis of first-order and total-effect sensitivity indices reveals the influential parameters on the output torque. The sensitivity analysis offers valuable insights into the significance of parameters and system optimization. Considering hysteresis deformation, this study presents a method for modeling and parameter estimation of the output torque in a heavy-duty robotic arm. The developed method contributes to the solution of practical problems and provides the groundwork for future research on SGHMC and parameter estimation algorithms.