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- W615858980 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." @default.
- W615858980 created "2016-06-24" @default.
- W615858980 creator A5008158408 @default.
- W615858980 creator A5012803680 @default.
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- W615858980 date "2015-06-03" @default.
- W615858980 modified "2023-09-24" @default.
- W615858980 title "Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach" @default.
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- W615858980 doi "https://doi.org/10.1371/journal.pone.0129157" @default.
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