Nonlinear 6-DOF stiffness modeling and identification for industrial robot

Stiffness modeling of industrial robots is a foundational technology for elucidating deformation mechanisms and enabling deformation compensation. Current research predominantly relies on a 1-degree-of-freedom (1-DOF) joint stiffness assumption, which inadequately characterizes the nonlinear stiffness behavior of industrial robots under complex six-dimensional end-effector loadings. Furthermore, existing 6-DOF stiffness models fail to comprehensively account for the nonlinear coupling effects between the end-effector wrench and configuration variations. To enhance deformation prediction accuracy, this paper proposes a 6-DOF nonlinear stiffness model that precisely characterizes these nonlinear properties by analytically modeling the coupling effects between the robot’s configuration and the end-effector wrench. Based on this model, a composite global optimization index is proposed to enhance parameters identification accuracy through the strategic selection of optimal robot configurations. Experimental results demonstrate that the proposed stiffness model achieves a significant reduction in deformation prediction errors, with the mean positional deformation prediction error reduced by 74% and the mean angular deformation prediction error reduced by 94% compared to an existing 1-DOF stiffness model.