Dynamic modeling and structural parameter optimization of Delta robot considering multi substructure coupling effects

Z. J. Xiao

doi:10.1080/15397734.2025.2522336

2025-07-01

PRE

DOI：10.1080/15397734.2025.2522336

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摘要

En 中文

The accuracy of the end effector can be affected by the elastic deformation of the arm resulting from external stimulation during the rapid operation of Delta parallel robots. In this study, the dynamic precise modeling of the Delta parallel robot is carried out by combining Lagrange and Newton-Euler methods. The influence of structural parameter changes on its vibration mechanism is analyzed, and the optimization model is designed to optimize the parameters of the original structure. The whole machine is divided into multiple rigid and flexible substructure coupling systems based on its structure and motion characteristics. The Lagrangian method and the finite element method are combined to establish a flexible substructure dynamics model. The influence of the elastic deformation of the flexible substructure is analyzed and applied to the rigid substructure, and then the dynamic model is established by Newton-Euler method. Considering the coupling effect between multiple rigid and flexible subsystems, the geometric constraint relationship is calculated and the dynamic model of the whole machine is established. Multiple approaches, including experiments and simulations, are used to verify the accuracy of the dynamic model. Based on the model’s rationality being confirmed, analyzing the influence trend of structural parameters on the key dynamic performance of the whole machine, structural parameter optimization is carried out based on PSO algorithm. The optimized structural parameters improve the robot positioning accuracy by 47.5% on the basis of the original structural parameters, and the maximum residual vibration value is reduced by 29.4%.

Keyword:

Delta parallel robot

dynamics modeling

rigid flexible coupling structure

vibration mechanism analysis

finite element method

structural parameter optimization