An enhanced dynamic identification method for 6-DOF industrial robot based on time-variant and weighted Genetic algorithm

This paper presents an identification method which is based on genetic algorithm (GA) and its improved method to estimate dynamic parameters of industrial robots without load. The procedure consists of the following steps: 1) derivation of the linear form of the dynamic model of the robot according to the Lagrange equation; 2) designing of the excitation trajectory in the form of fifth order Fourier series as exciting trajectory; 3) identification, where genetic algorithm is used to find the global optimal parameters through the genetic exchange between the groups and the survival of the fittest mechanism with the minimum variance between the theoretical torque and the actual torque as the optimization criteria; 4) model validation; 5) analysis of the factors influencing the accuracy of the results in the identification process; 6) proposal of improved method. The experimental results show that the predicted torque and the measured torque obtained by the identification algorithm have a high matching degree, and the model can reflect the actual dynamic characteristics of the robot.