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

• Published in: PloS one Vol. 10; no. 6; p. e0129157
• Main Authors: Yan, Danping, Lu, Yongzhong, Levy, David
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.06.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Applied research; Artificial Intelligence; CAD; Computer aided design; Control; Control systems design; Cybernetics; Design and construction; Earth science; Experiments; Genetic algorithms; Heuristic programming; Heuristics; Humans; Hybridization; Image coding; Information science; Machine learning; Manipulators; Mathematical models; Models, Statistical; Neural networks; Noise; Optimization; Optimization techniques; Parameter estimation; Parameter identification; Particle swarm optimization; Performance evaluation; Proportional integral derivative; Remote sensing; Research methodology; Robot arms; Robotics - instrumentation; Robotics - statistics & numerical data; Robots; Signal processing; Spectrum analysis; Swarm intelligence; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0129157

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.