Citation: Tang, M.; Yan, Y.; An, B.;
Wang, W.; Zhang, Y. Dynamic
Parameter Identification of
Collaborative Robot Based on
WLS-RWPSO Algorithm. Machines
2023, 11, 316. https://doi.org/
10.3390/machines11020316
Academic Editor: Dan Zhang
Received: 13 January 2023
Revised: 8 February 2023
Accepted: 18 February 2023
Published: 20 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Dynamic Parameter Identification of Collaborative Robot Based
on WLS-RWPSO Algorithm
Minan Tang
1,
* , Yaguang Yan
1
, Bo An
1
, Wenjuan Wang
2
and Yaqi Zhang
1
1
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2
School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
* Correspondence: tangminan@mail.lzjtu.cn; Tel.: +86-138-9368-8178
Abstract:
Parameter identification of the dynamic model of collaborative robots is the basis of
the development of collaborative robot motion state control, path tracking, state monitoring, fault
diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research.
Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes
an identification algorithm based on weighted least squares and random weighted particle swarm
optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established
using the Lagrangian method, the dynamic parameters of the robot to be identified are determined,
and the linear form of the dynamics model of the robot is derived taking into account the joint friction
characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of
the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a
random weight particle swarm optimization algorithm is proposed for the local optimal problem to
identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as
the excitation trajectory, and the original data collected by the sensor are denoised and smoothed
by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom
collaborative robot proves that the predicted torque obtained by the identification algorithm in this
paper has a high degree of matching with the measured torque, and the established model can reflect
the dynamic characteristics of the robot, effectively improving the identification accuracy.
Keywords:
collaborative robot; parameter identification; weighted least squares method; random
weight particle swarm algorithm; Kalman filter
1. Introduction
At present, robot technology is developing towards intelligence, and the manufactur-
ing mode is also changing. In recent years, collaborative robots have received extensive
attention and research around the world. According to the definition in ISO10218-2, a robot
that can interact directly with humans in a designated collaborative area is called a collabora-
tive robot. Compared with traditional industrial robots, collaborative robots have the benefits
of high security, good versatility, sensitivity, precision, ease of use, and human–machine
collaboration. The above advantages make collaborative robots not only applicable in the
manufacturing field, but also gives them potential application value in the fields of home
service and rehabilitation medicine—for example, compliant robotic arms in the industrial
field, surgical robots in the medical field, wearable rehabilitation assistance robots, and anti-
terrorist and explosion-proof robots in special applications [
1
,
2
]. Utilizing the technology of
human–machine fusion, the establishment of a fusion robot technology with intrinsic safety,
human–machine collaborative cognition, and behavioral mutual assistance can provide
support for emerging new application scenarios such as industry, service, and medical care.
To break through the challenges of existing robots in the four aspects of environmental
adaptability, task adaptability, safety, and interactive capabilities, it is urgent to study a
new generation of human–machine fusion robots [3,4].
Machines 2023, 11, 316. https://doi.org/10.3390/machines11020316 https://www.mdpi.com/journal/machines
