Forward kinematic algorithm for 3-DOF parallel platform based on the integration of deep residual network and error compensation
ZHOU Wei
CHEN Fan
HU Yi
YAN Xusen
ZHANG Ju
SU Shijie
Abstract：[Objective]Efficient and accurate solutions to the forward kinematic problem are critical technical challenges for achieving real-time control of parallel platforms.Existing algorithms,however,often exhibit poor generalizability,low computational efficiency,and limited accuracy.A forward kinematic solution algorithm for parallel platforms integrating a deep residual network and error compensation was proposed to address these issues.[Methods]Firstly,the nonlinear mapping from joint space to pose space was realized using the proposed deep residual network model.Secondly,an iterative process with an error compensation strategy was adopted to further improve the computational accuracy of the deep residual network model.Finally,simulation analysis was carried out on two 3-DOF parallel platforms with different configurations.[Results]The results show that the efficiency of the proposed algorithm is improved by approximately 91%,87%,and 58%at minimum compared with the Newton-Raphson method,the back propagation-Newton Raphson hybrid strategy algorithm,and the quasi-Newton method,respectively.The proposed algorithm can efficiently solve the forward kinematic problem of 3-DOF parallel platforms,meet the computational requirements for real-time control of parallel platforms,and be easily applied to other configurations of parallel platforms.
Keywords：Parallel mechanismForward kinematicDeep learningNeural network
Publication Date：2026-04-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 11-20 )