Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

Abstract

Purpose Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm. Design/methodology/approach This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification. Findings Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved. Originality/value A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Parameters identification and contact interaction control of redundant robot based on dynamic model

“…Where, t is driving torque, M q ( ) is inertia matrix, C q q , ( )  is matrix of Coriolis force, G q ( ) is gravity matrix, f q ( )  is friction force, J q T ( ) is transpose matrix of J q , ( ) and F is external force [20][21][22]. Each link has 10 dynamic parameters.…”