Parameter identification of robot manipulators: a heuristic particle swarm search approach

Danping Yan; Yongzhong Lu; David Levy

doi:10.1371/journal.pone.0129157

Parameter identification of robot manipulators: a heuristic particle swarm search approach

PLoS One. 2015 Jun 3;10(6):e0129157. doi: 10.1371/journal.pone.0129157. eCollection 2015.

Authors

Danping Yan¹, Yongzhong Lu², David Levy³

Affiliations

¹ College of Public Administration, Huazhong University of Science and Technology, Wuhan, Hubei, China; Non-traditional Security Center of Huazhong University of Science and Technology, Wuhan, Hubei, China.
² School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
³ Faculty of Engineering and Information Technologies, University of Sydney, Sydney, New South Wales, Australia.

Abstract

Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence
Heuristics*
Humans
Models, Statistical*
Robotics / instrumentation
Robotics / statistics & numerical data*

Grants and funding

The authors declare that this work is supported by National 985 Project of Non-traditional Security at Huazhong University of Science and Technology, and is also funded by the Fundamental Research Funds for the Central Universities, HUST: 2014AA027. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.