Parameter Identification for the Dynamics Model of a Robotic Grasping Process Based on SFOA

To precisely simulate the nonlinear dynamic characteristics of a robotic arm grasping cylindrical objects from storage units, this study establishes a dynamic model of the robotic grasping process incorporating Coulomb and viscous friction models to characterize frictional properties. Furthermore, to effectively identify unknown parameters in the dynamic model, a parameter identification method based on the Superb Fairy-wren Optimization Algorithm (SFOA) is proposed. The root-mean-square error (RMSE) between the displacement responses from the dynamic model and the experimentally acquired displacement data serves as the optimization objective. Multiple sets of experimental data are utilized to identify the unknown parameters of the dynamic model. The results demonstrate that when the identified parameters are applied to the dynamic model, the goodness-of-fit between the model’s response displacement data and the experimental displacement data exceeds 0.999. This validates the effectiveness and accuracy of the proposed method for identifying unknown parameters in dynamic models.