Dynamic parameter identification method derived from dynamic forgetting factor recursive least square method

WenHao Lv; Mei Liu; JinMing Liu; XingChen Zhu; ZhenYu Zhang

doi:10.1117/12.2625337

12 December 2021 Dynamic parameter identification method derived from dynamic forgetting factor recursive least square method

WenHao Lv, Mei Liu, JinMing Liu, XingChen Zhu, ZhenYu Zhang

Author Affiliations +

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 1212720 (2021) https://doi.org/10.1117/12.2625337
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

Abstract

In order to improve the accuracy of dynamic parameter identification of SCARA robot, a dynamic parameter identification method derived from dynamic forgetting factor recursive least square method is proposed. Using the Newton-Euler method to establish a robot kinetic model and linearize, select the five-order Fourier grade as an excitation trajectory, and use the genetic algorithm to obtain the optimal excitation trajectory coefficient. On the basis of being extracted, the dynamic adjustment mechanism of the forgetting factor is designed, and the convergence speed of the algorithm is improved. Finally, MATLAB and ADAMS were used to combine simulation experiments, and the minimum inertial parameters of robots were obtained. In order to verify the accuracy of the parameter identification and the validity of the identification algorithm, the trajectory different from the excitation trajectory is randomly selected as input. The reliability of the identification algorithm and the accuracy of the identification algorithm is proved by contrast the synonymous model and the Idams physical model output.

Citation Download Citation

WenHao Lv, Mei Liu, JinMing Liu, XingChen Zhu, and ZhenYu Zhang "Dynamic parameter identification method derived from dynamic forgetting factor recursive least square method", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 1212720 (12 December 2021); https://doi.org/10.1117/12.2625337

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available