Particle Swarm Optimization Based Parameter Identification Applied To A Target Tracker Robot With Flexible Joint

This paper focuses on parameter identification of a target tracker robot possessing flexible joints using particle swarm optimization (pso) algorithm. since, belt and pulley mechanisms are known as flexible joints in robotic systems, their elastic behavior affecting a tracker robot is investigated in this work. first, dynamic equations governing the robot behavior are extracted taking into account the effects of considered flexible joints. thus, a flexible joint is modeled by a non-linear spring and damper system connecting the motor to the link. it is found that the governing dynamic equations include some unknown parameters, which must be identified in order to design the robot system. consequently, a pso-based identification scheme is proposed to achieve the unknown variables based on the experimental data of the open-loop system. lastly, for validating the proposed identification scheme, the obtained results are compared to the experimental measurements as well as the results of another similar work in which the robot is modeled with rigid joints. the consequences reveal that the mathematical model of the robot with flexible joint can describe the elastic behavior of the tracker robot. thus, a better agreement between the simulation and experimental data are found in comparison with outcomes of the robot model with rigid joints.